sf-ai-agentscript — Skillopedia

SF-AI-AgentScript Skill Agent Script is the code-first path for deterministic Agentforce agents. Use this skill when the user is authoring files, building finite-state topic flows, or needs repeatable control over routing, variables, actions, and publish behavior. Start with the shortest guide first: references/activation-checklist.md Migrating from the Builder UI? Use references/migration-guide.md When This Skill Owns the Task Use when the work involves: - creating or editing files - deterministic topic routing, guards, and transitions - Agent Script CLI workflows ( , , , , ) - slot filling,…

)\n CONNECTION_BLOCK_PATTERN = re.compile(r\"^connection\\s+([A-Za-z_][A-Za-z0-9_]*)\\s*:\")\n KEY_VALUE_PATTERN = re.compile(r\"^([A-Za-z_][A-Za-z0-9_:]*)\\s*:\\s*(.*)$\")\n\n RESERVED_CONTEXT_VARIABLE_NAMES = {\n \"Locale\",\n \"Channel\",\n \"Status\",\n \"Origin\",\n }\n\n RESERVED_FIELD_NAMES = {\n \"description\",\n \"label\",\n \"is_required\",\n \"is_displayable\",\n \"is_used_by_planner\",\n \"model\",\n }\n\n INVALID_TRANSITION_PROPERTIES = {\n \"label\",\n \"require_user_confirmation\",\n \"include_in_progress_indicator\",\n \"progress_indicator_message\",\n \"is_required\",\n \"is_user_input\",\n \"is_displayable\",\n \"is_used_by_planner\",\n }\n\n TOP_LEVEL_ENDERS = (\n \"config:\",\n \"variables:\",\n \"system:\",\n \"knowledge:\",\n \"language:\",\n \"connection \",\n \"connections:\",\n \"topic \",\n \"start_agent \",\n \"actions:\",\n )\n\n CONDITIONAL_EXECUTABLE_PREFIXES = (\n \"|\",\n \"if \",\n \"else:\",\n \"run \",\n \"set \",\n \"transition to \",\n \"with \",\n )\n\n SENSITIVE_ACTION_TOKENS = (\n \"delete\",\n \"refund\",\n \"charge\",\n \"cancel\",\n \"escalate\",\n \"transfer\",\n \"close\",\n \"disable\",\n \"update_profile\",\n \"update_customer\",\n \"reset_password\",\n \"change_owner\",\n )\n\n LARGE_FILE_LIMITS = {\n \"lines\": 450,\n \"topics\": 18,\n \"actions\": 40,\n }\n\n def __init__(self, content: str, file_path: str):\n self.content = content\n self.file_path = file_path\n self.lines = content.split(\"\\n\")\n self.errors: List[Tuple[int, str, str]] = []\n self.warnings: List[Tuple[int, str, str]] = []\n\n self.config_fields: Dict[str, Tuple[str, int]] = {}\n self.config_raw_fields: Dict[str, Tuple[str, int]] = {}\n self.topic_names: Dict[str, int] = {}\n self.start_agent_names: Dict[str, int] = {}\n self.variable_names: Dict[str, int] = {}\n self.variable_definitions: List[Dict] = []\n self.variable_by_name: Dict[str, Dict] = {}\n self.defined_topics: Set[str] = set()\n self.connection_blocks: List[Dict] = []\n self.connection_messaging_lines: List[int] = []\n self.messaging_linked_var_lines: List[int] = []\n self.context_linked_var_lines: List[int] = []\n self.action_definitions: List[Dict] = []\n self.block_descriptions: List[Dict] = []\n self.multiline_description_issues: List[Tuple[int, str]] = []\n self.lifecycle_instruction_wrappers: List[Tuple[int, str]] = []\n self.lifecycle_pipe_lines: List[Tuple[int, str]] = []\n self.lifecycle_run_lines: List[Tuple[int, str, str]] = []\n self.lifecycle_arithmetic_lines: List[Tuple[int, str, str, str]] = [] # (line, lifecycle_name, var_name, owner)\n self.lifecycle_null_guards: List[Tuple[int, str, str, str]] = [] # (line, lifecycle_name, var_name, owner)\n self.topic_inline_system_lines: List[Tuple[int, str]] = []\n self.line_owner: Dict[int, str] = {}\n self.welcome_error_inline_interpolation_lines: List[Tuple[int, str]] = []\n self.welcome_error_folded_scalar_lines: List[Tuple[int, str]] = []\n self.default_agent_user_comment_lines: List[int] = []\n self.top_level_actions_lines: List[int] = []\n\n self.validation_org = self._resolve_validation_org()\n self._query_cache: Dict[str, Dict] = {}\n self._user_query_cache: Dict[str, Dict] = {}\n\n self._parse_structure()\n\n @staticmethod\n def _indent(raw_line: str) -> int:\n expanded = raw_line.expandtabs(4)\n return len(expanded) - len(expanded.lstrip(\" \"))\n\n @staticmethod\n def _strip_quotes(value: str) -> str:\n value = value.strip()\n if len(value) >= 2 and ((value[0] == '\"' and value[-1] == '\"') or (value[0] == \"'\" and value[-1] == \"'\")):\n return value[1:-1]\n return value\n\n def _strip_inline_comment(self, value: str) -> str:\n in_single = False\n in_double = False\n escaped = False\n out: List[str] = []\n for char in value:\n if char == \"\\\\\" and (in_single or in_double):\n escaped = not escaped\n out.append(char)\n continue\n if char == \"'\" and not in_double and not escaped:\n in_single = not in_single\n elif char == '\"' and not in_single and not escaped:\n in_double = not in_double\n elif char == \"#\" and not in_single and not in_double:\n break\n out.append(char)\n escaped = False\n return \"\".join(out).rstrip()\n\n def _clean_scalar_value(self, value: str) -> str:\n commentless = self._strip_inline_comment(value).strip()\n return self._strip_quotes(commentless)\n\n @staticmethod\n def _sql_quote(value: str) -> str:\n return value.replace(\"'\", \"\\\\'\")\n\n @staticmethod\n def _is_placeholder_value(value: Optional[str]) -> bool:\n return bool(value and \"{{\" in value and \"}}\" in value)\n\n @staticmethod\n def _is_demo_user_value(value: Optional[str]) -> bool:\n if not value:\n return False\n lowered = value.lower()\n demo_domains = (\"yourorg.com\", \"company.com\", \"company.salesforce.com\", \"example.com\")\n return AgentScriptValidator._is_placeholder_value(value) or any(lowered.endswith(f\"@{domain}\") for domain in demo_domains)\n\n def _issue_message(self, rule_id: Optional[str], message: str) -> str:\n return f\"[{rule_id}] {message}\" if rule_id else message\n\n def _add_error(self, line_num: int, message: str, rule_id: Optional[str] = None):\n self.errors.append((line_num, \"error\", self._issue_message(rule_id, message)))\n\n def _add_warning(self, line_num: int, message: str, rule_id: Optional[str] = None):\n self.warnings.append((line_num, \"warning\", self._issue_message(rule_id, message)))\n\n def _project_root_for_file(self) -> Path:\n current = Path(self.file_path).resolve().parent\n for candidate in [current, *current.parents]:\n if (candidate / \"sfdx-project.json\").exists():\n return candidate\n return current\n\n def _resolve_sf_target_org(self, cwd: Path) -> Optional[str]:\n try:\n proc = subprocess.run(\n [\"sf\", \"config\", \"get\", \"target-org\", \"--json\"],\n text=True,\n capture_output=True,\n timeout=15,\n cwd=str(cwd),\n )\n except Exception:\n return None\n\n if proc.returncode != 0:\n return None\n\n try:\n payload = json.loads(proc.stdout or \"{}\")\n except Exception:\n return None\n\n results = payload.get(\"result\") or []\n if not isinstance(results, list):\n return None\n\n for entry in results:\n value = (entry or {}).get(\"value\")\n if value:\n return str(value).strip()\n return None\n\n def _resolve_validation_org(self) -> Optional[str]:\n for env_name in (\"AGENTSCRIPT_VALIDATION_ORG\", \"SF_TARGET_ORG\", \"TARGET_ORG\"):\n value = os.environ.get(env_name)\n if value:\n return value.strip()\n return self._resolve_sf_target_org(self._project_root_for_file())\n\n def _effective_agent_type(self) -> Optional[str]:\n agent_type = self.config_fields.get(\"agent_type\")\n default_agent_user = self.config_fields.get(\"default_agent_user\")\n if agent_type:\n return agent_type[0]\n if default_agent_user:\n return \"AgentforceServiceAgent\"\n return None\n\n def _run_soql_query(self, soql: str) -> Dict:\n if soql in self._query_cache:\n return self._query_cache[soql]\n\n if not self.validation_org:\n result = {\"ok\": False, \"reason\": \"no_validation_org\"}\n self._query_cache[soql] = result\n return result\n\n try:\n proc = subprocess.run(\n [\"sf\", \"data\", \"query\", \"--query\", soql, \"-o\", self.validation_org, \"--json\"],\n text=True,\n capture_output=True,\n timeout=30,\n )\n except Exception as exc:\n result = {\"ok\": False, \"reason\": \"query_failed\", \"detail\": str(exc)}\n self._query_cache[soql] = result\n return result\n\n if proc.returncode != 0:\n detail = (proc.stdout or proc.stderr or \"sf data query failed\").strip()\n result = {\"ok\": False, \"reason\": \"query_failed\", \"detail\": detail}\n self._query_cache[soql] = result\n return result\n\n try:\n payload = json.loads(proc.stdout or \"{}\")\n except Exception:\n result = {\"ok\": False, \"reason\": \"query_failed\", \"detail\": \"Could not parse sf data query JSON output.\"}\n self._query_cache[soql] = result\n return result\n\n result = {\n \"ok\": True,\n \"payload\": payload,\n \"records\": ((payload.get(\"result\") or {}).get(\"records\") or []) if isinstance(payload, dict) else [],\n }\n self._query_cache[soql] = result\n return result\n\n def _query_user_in_org(self, username: str) -> Dict:\n if username in self._user_query_cache:\n return self._user_query_cache[username]\n\n soql = (\n \"SELECT Username, IsActive, UserType, Profile.Name \"\n f\"FROM User WHERE Username = '{self._sql_quote(username)}' LIMIT 1\"\n )\n query = self._run_soql_query(soql)\n if not query.get(\"ok\"):\n result = {\"ok\": False, \"reason\": query.get(\"reason\"), \"detail\": query.get(\"detail\")}\n self._user_query_cache[username] = result\n return result\n\n records = query.get(\"records\") or []\n if not records:\n result = {\"ok\": False, \"reason\": \"missing\"}\n self._user_query_cache[username] = result\n return result\n\n record = records[0]\n is_active = record.get(\"IsActive\") is True\n user_type = record.get(\"UserType\")\n profile_name = ((record.get(\"Profile\") or {}).get(\"Name\"))\n\n if not is_active:\n result = {\"ok\": False, \"reason\": \"inactive\", \"record\": record}\n elif user_type == \"AutomatedProcess\":\n result = {\"ok\": False, \"reason\": \"automated_process\", \"record\": record}\n elif profile_name != \"Einstein Agent User\":\n result = {\"ok\": False, \"reason\": \"wrong_profile\", \"record\": record}\n else:\n result = {\"ok\": True, \"reason\": \"valid\", \"record\": record}\n\n self._user_query_cache[username] = result\n return result\n\n def _query_permission_set_assignments(self, username: str) -> Dict:\n soql = (\n \"SELECT PermissionSetId, PermissionSet.Name \"\n f\"FROM PermissionSetAssignment WHERE Assignee.Username = '{self._sql_quote(username)}'\"\n )\n return self._run_soql_query(soql)\n\n def _query_permission_set(self, permset_name: str) -> Dict:\n soql = f\"SELECT Id, Name FROM PermissionSet WHERE Name = '{self._sql_quote(permset_name)}' LIMIT 1\"\n return self._run_soql_query(soql)\n\n def _query_apex_classes(self, class_names: List[str]) -> Dict:\n names = sorted({name for name in class_names if name})\n if not names:\n return {\"ok\": True, \"records\": []}\n quoted = \", \".join(f\"'{self._sql_quote(name)}'\" for name in names)\n return self._run_soql_query(f\"SELECT Id, Name FROM ApexClass WHERE Name IN ({quoted})\")\n\n def _query_setup_entity_access(self, parent_id: str, setup_entity_ids: List[str], setup_entity_type: str = \"ApexClass\") -> Dict:\n ids = sorted({entity_id for entity_id in setup_entity_ids if entity_id})\n if not ids:\n return {\"ok\": True, \"records\": []}\n quoted = \", \".join(f\"'{entity_id}'\" for entity_id in ids)\n soql = (\n \"SELECT SetupEntityId, SetupEntityType \"\n f\"FROM SetupEntityAccess WHERE ParentId = '{parent_id}' \"\n f\"AND SetupEntityType = '{setup_entity_type}' AND SetupEntityId IN ({quoted})\"\n )\n return self._run_soql_query(soql)\n\n def _query_active_flows(self, flow_names: List[str]) -> Dict:\n names = sorted({name for name in flow_names if name})\n if not names:\n return {\"ok\": True, \"records\": []}\n quoted = \", \".join(f\"'{self._sql_quote(name)}'\" for name in names)\n soql = f\"SELECT ApiName, ActiveVersionId FROM FlowDefinitionView WHERE ApiName IN ({quoted})\"\n return self._run_soql_query(soql)\n\n def _agent_identifier(self) -> Optional[str]:\n if \"developer_name\" in self.config_fields:\n return self.config_fields[\"developer_name\"][0]\n if \"agent_name\" in self.config_fields:\n return self.config_fields[\"agent_name\"][0]\n return None\n\n def _collect_targets(self) -> Dict[str, List[Tuple[str, int, str]]]:\n apex_targets: List[Tuple[str, int, str]] = []\n flow_targets: List[Tuple[str, int, str]] = []\n other_targets: List[Tuple[str, int, str]] = []\n\n for action in self.action_definitions:\n if action.get(\"scope\") != \"definition\":\n continue\n target = action.get(\"target\")\n if not target:\n continue\n line = action.get(\"target_line\") or action.get(\"line\") or 1\n if target.startswith(\"apex://\"):\n apex_ref = target[len(\"apex://\"):].strip()\n apex_class_name = apex_ref.split(\".\", 1)[0]\n apex_targets.append((apex_class_name, line, target))\n elif target.startswith(\"flow://\"):\n flow_name = target[len(\"flow://\"):].strip()\n flow_targets.append((flow_name, line, target))\n else:\n other_targets.append((target, line, target))\n\n return {\"apex\": apex_targets, \"flow\": flow_targets, \"other\": other_targets}\n\n def _collect_connection_route_targets(self) -> List[Tuple[str, int, str]]:\n flows: List[Tuple[str, int, str]] = []\n for block in self.connection_blocks:\n route_name = (block.get(\"fields\") or {}).get(\"outbound_route_name\")\n if not route_name:\n continue\n value, line = route_name\n if value.startswith(\"flow://\"):\n flows.append((value[len(\"flow://\"):].strip(), line, value))\n return flows\n\n def _flush_action(self, current_action: Optional[Dict]):\n if current_action:\n self.action_definitions.append(current_action)\n\n def _parse_structure(self):\n current_top: Optional[str] = None\n current_block_name: Optional[str] = None\n current_action: Optional[Dict] = None\n current_variable: Optional[Dict] = None\n current_connection: Optional[Dict] = None\n actions_mode: Optional[str] = None\n actions_indent: Optional[int] = None\n reasoning_indent: Optional[int] = None\n current_io: Optional[Dict] = None\n current_io_field: Optional[Dict] = None\n lifecycle_block: Optional[Dict] = None\n\n for i, raw_line in enumerate(self.lines, 1):\n indent = self._indent(raw_line)\n stripped = raw_line.strip()\n\n if current_top in {\"topic\", \"start_agent\"} and current_block_name:\n self.line_owner[i] = current_block_name\n\n if not stripped:\n continue\n\n if stripped.startswith(\"#\"):\n continue\n\n # Close nested contexts when indentation decreases\n if lifecycle_block and indent \u003c= lifecycle_block[\"indent\"]:\n lifecycle_block = None\n\n if current_io_field and indent \u003c= current_io_field[\"indent\"]:\n # Check filter_from_agent + is_used_by_planner conflict before closing\n if current_io_field.get(\"has_filter_from_agent\") and current_io_field.get(\"has_is_used_by_planner\") and current_action:\n current_action[\"filter_planner_conflict_lines\"].append(\n (current_io_field[\"is_used_by_planner_line\"], current_io_field[\"name\"], current_io_field[\"section\"])\n )\n current_io_field = None\n\n if current_io and indent \u003c= current_io[\"indent\"]:\n current_io = None\n\n if current_action and indent \u003c= current_action[\"indent\"]:\n self._flush_action(current_action)\n current_action = None\n current_io = None\n current_io_field = None\n\n if current_variable and (current_top != \"variables\" or indent \u003c= current_variable[\"indent\"]):\n current_variable = None\n\n if actions_mode and actions_indent is not None and indent \u003c= actions_indent:\n actions_mode = None\n actions_indent = None\n\n if reasoning_indent is not None and indent \u003c= reasoning_indent:\n reasoning_indent = None\n\n if indent == 0 and current_connection and not stripped.startswith(\"connection \"):\n self.connection_blocks.append(current_connection)\n current_connection = None\n\n # Top-level blocks\n if indent == 0:\n current_top = None\n current_block_name = None\n lifecycle_block = None\n\n if stripped == \"config:\":\n current_top = \"config\"\n continue\n if stripped == \"variables:\":\n current_top = \"variables\"\n continue\n if stripped == \"knowledge:\":\n current_top = \"knowledge\"\n continue\n if stripped == \"language:\":\n current_top = \"language\"\n continue\n if stripped == \"system:\":\n current_top = \"system\"\n continue\n if stripped == \"connections:\":\n current_top = \"connections\"\n continue\n if stripped == \"actions:\":\n self.top_level_actions_lines.append(i)\n current_top = \"actions\"\n continue\n\n connection_match = self.CONNECTION_BLOCK_PATTERN.match(stripped)\n if connection_match:\n if current_connection:\n self.connection_blocks.append(current_connection)\n channel = connection_match.group(1)\n current_top = \"connection\"\n current_connection = {\"channel\": channel, \"line\": i, \"fields\": {}}\n if channel == \"messaging\":\n self.connection_messaging_lines.append(i)\n continue\n\n block_match = self.BLOCK_NAME_PATTERN.match(stripped)\n if block_match:\n kind, name = block_match.groups()\n current_top = kind\n current_block_name = name\n self.line_owner[i] = name\n if kind == \"topic\":\n self.topic_names.setdefault(name, i)\n else:\n self.start_agent_names.setdefault(name, i)\n self.defined_topics.add(name)\n continue\n\n # config block\n if current_top == \"config\" and indent > 0:\n field_match = self.KEY_VALUE_PATTERN.match(stripped)\n if field_match:\n field, raw_value = field_match.groups()\n self.config_raw_fields[field] = (raw_value, i)\n cleaned = self._clean_scalar_value(raw_value)\n self.config_fields[field] = (cleaned, i)\n if field == \"default_agent_user\" and self._strip_inline_comment(raw_value).strip() != raw_value.strip():\n self.default_agent_user_comment_lines.append(i)\n continue\n\n # variables block\n if current_top == \"variables\" and indent > 0:\n commentless = self._strip_inline_comment(stripped)\n var_match = self.VARIABLE_DECL_PATTERN.match(commentless)\n if var_match:\n name, modifier, var_type, default_value = var_match.groups()\n definition = {\n \"name\": name,\n \"modifier\": modifier.lower(),\n \"type\": var_type,\n \"line\": i,\n \"indent\": indent,\n \"default\": default_value.strip() if default_value else None,\n \"source\": None,\n \"source_line\": None,\n }\n self.variable_names.setdefault(name, i)\n self.variable_definitions.append(definition)\n self.variable_by_name[name] = definition\n current_variable = definition\n if name in self.RESERVED_FIELD_NAMES:\n # Report later in dedicated rule.\n pass\n continue\n\n # Multi-line variable declaration: \"Name:\" on its own line,\n # with type/modifier/default on subsequent indented lines.\n # This captures the variable name for reserved-name checks\n # even when the full single-line pattern doesn't match.\n multiline_var_match = re.match(r\"^([A-Za-z][A-Za-z0-9_]*)\\s*:\\s*$\", commentless)\n if multiline_var_match:\n name = multiline_var_match.group(1)\n definition = {\n \"name\": name,\n \"modifier\": \"\",\n \"type\": \"\",\n \"line\": i,\n \"indent\": indent,\n \"default\": None,\n \"source\": None,\n \"source_line\": None,\n }\n self.variable_names.setdefault(name, i)\n self.variable_definitions.append(definition)\n self.variable_by_name[name] = definition\n current_variable = definition\n continue\n\n if current_variable and indent > current_variable[\"indent\"]:\n field_match = self.KEY_VALUE_PATTERN.match(stripped)\n if field_match:\n field, raw_value = field_match.groups()\n cleaned = self._clean_scalar_value(raw_value)\n if field == \"type\" and not current_variable.get(\"type\"):\n current_variable[\"type\"] = cleaned\n elif field == \"default\" and current_variable.get(\"default\") is None:\n current_variable[\"default\"] = cleaned\n elif field == \"source\":\n current_variable[\"source\"] = cleaned\n current_variable[\"source_line\"] = i\n if \"@MessagingSession.\" in cleaned or \"@MessagingEndUser.\" in cleaned:\n self.messaging_linked_var_lines.append(i)\n if cleaned.startswith(\"@context.\"):\n self.context_linked_var_lines.append(i)\n continue\n\n # connection block\n if current_top == \"connection\" and current_connection and indent > 0:\n field_match = self.KEY_VALUE_PATTERN.match(stripped)\n if field_match:\n field, raw_value = field_match.groups()\n current_connection[\"fields\"][field] = (self._clean_scalar_value(raw_value), i)\n continue\n\n # system block (simple heuristics for welcome/error message guidance)\n if current_top == \"system\" and indent > 0:\n lower = stripped.lower()\n if \"welcome\" in lower or \"error\" in lower:\n kind = \"welcome\" if \"welcome\" in lower else \"error\"\n if \"{!\" in stripped:\n self.welcome_error_inline_interpolation_lines.append((i, kind))\n if re.search(r\":\\s*>[-+]?\\s*$\", stripped):\n self.welcome_error_folded_scalar_lines.append((i, kind))\n continue\n\n # topic/start_agent internals\n if current_top in {\"topic\", \"start_agent\"} and indent > 0:\n # lifecycle tracking\n if stripped in {\"before_reasoning:\", \"after_reasoning:\"}:\n lifecycle_block = {\"name\": stripped[:-1], \"indent\": indent, \"owner\": current_block_name}\n continue\n\n if lifecycle_block and indent > lifecycle_block[\"indent\"]:\n if stripped.startswith(\"instructions:\"):\n self.lifecycle_instruction_wrappers.append((i, lifecycle_block[\"name\"]))\n if stripped.startswith(\"|\"):\n self.lifecycle_pipe_lines.append((i, lifecycle_block[\"name\"]))\n run_match = re.match(r\"^run\\s+@actions\\.([A-Za-z_][A-Za-z0-9_]*)\\b\", stripped)\n if run_match:\n self.lifecycle_run_lines.append((i, lifecycle_block[\"name\"], run_match.group(1)))\n # Detect arithmetic on variables: set @variables.X = @variables.X + N\n arith_match = re.match(\n r\"^set\\s+@variables\\.([A-Za-z_][A-Za-z0-9_]*)\\s*=\\s*@variables\\.\\1\\s*[+\\-]\\s*\\d+\",\n stripped,\n )\n if arith_match:\n self.lifecycle_arithmetic_lines.append((i, lifecycle_block[\"name\"], arith_match.group(1), lifecycle_block.get(\"owner\") or \"\"))\n # Detect null guards: if @variables.X is None:\n null_match = re.match(r\"^if\\s+@variables\\.([A-Za-z_][A-Za-z0-9_]*)\\s+is\\s+None\\s*:\", stripped)\n if null_match:\n self.lifecycle_null_guards.append((i, lifecycle_block[\"name\"], null_match.group(1), lifecycle_block.get(\"owner\") or \"\"))\n # keep parsing deeper structures too\n\n if stripped == \"reasoning:\":\n reasoning_indent = indent\n continue\n\n if stripped == \"actions:\":\n if reasoning_indent is not None and indent > reasoning_indent:\n actions_mode = \"reasoning\"\n else:\n actions_mode = \"definition\"\n actions_indent = indent\n continue\n\n if actions_mode in {\"definition\", \"reasoning\"} and actions_indent is not None and indent > actions_indent:\n if current_action is None or indent == current_action[\"indent\"]:\n action_match = self.ACTION_DECL_PATTERN.match(stripped)\n if action_match:\n name, remainder = action_match.groups()\n if name not in {\"inputs\", \"outputs\", \"target\", \"description\", \"label\", \"source\", \"system\", \"reasoning\"}:\n current_action = {\n \"name\": name,\n \"line\": i,\n \"indent\": indent,\n \"owner\": current_block_name,\n \"scope\": actions_mode,\n \"kind\": \"definition\" if actions_mode == \"definition\" else \"reasoning\",\n \"inline\": remainder.strip(),\n \"target\": None,\n \"target_line\": None,\n \"description\": None,\n \"description_line\": None,\n \"has_available_when\": False,\n \"available_when_count\": 0,\n \"has_inputs_block\": False,\n \"has_outputs_block\": False,\n \"io_fields\": [],\n \"invalid_transition_properties\": [],\n \"required_input_lines\": [],\n \"prompt_output_display_lines\": [],\n \"date_io_lines\": [],\n \"require_user_confirmation_lines\": [],\n \"reserved_io_field_lines\": [],\n \"filter_planner_conflict_lines\": [],\n }\n inline = remainder.strip()\n if inline.startswith(\"@utils.transition\"):\n current_action[\"kind\"] = \"utility_transition\"\n elif inline.startswith(\"@topic.\"):\n current_action[\"kind\"] = \"delegation\"\n elif inline.startswith(\"@utils.\"):\n current_action[\"kind\"] = \"utility\"\n elif inline.startswith(\"target:\"):\n target_value = self._clean_scalar_value(inline.split(\":\", 1)[1].strip())\n current_action[\"target\"] = target_value\n current_action[\"target_line\"] = i\n continue\n\n if current_action is not None and indent > current_action[\"indent\"]:\n if stripped.startswith(\"target:\"):\n target = self._clean_scalar_value(stripped.split(\":\", 1)[1].strip())\n current_action[\"target\"] = target\n current_action[\"target_line\"] = i\n continue\n\n if stripped.startswith(\"description:\"):\n current_action[\"description\"] = self._clean_scalar_value(stripped.split(\":\", 1)[1].strip())\n current_action[\"description_line\"] = i\n continue\n\n if stripped.startswith(\"available when\"):\n current_action[\"has_available_when\"] = True\n current_action[\"available_when_count\"] += 1\n continue\n\n if stripped == \"inputs:\":\n current_action[\"has_inputs_block\"] = True\n current_io = {\"name\": \"inputs\", \"indent\": indent}\n current_io_field = None\n continue\n\n if stripped == \"outputs:\":\n current_action[\"has_outputs_block\"] = True\n current_io = {\"name\": \"outputs\", \"indent\": indent}\n current_io_field = None\n continue\n\n if stripped.startswith(\"require_user_confirmation:\"):\n value = self._clean_scalar_value(stripped.split(\":\", 1)[1].strip())\n if value == \"True\":\n current_action[\"require_user_confirmation_lines\"].append(i)\n continue\n\n if current_io and indent > current_io[\"indent\"]:\n if current_io_field is None or indent == current_io_field[\"indent\"]:\n field_match = self.IO_FIELD_PATTERN.match(self._strip_inline_comment(stripped))\n if field_match:\n quoted_field_name, plain_field_name, field_type = field_match.groups()\n field_name = quoted_field_name or plain_field_name\n current_io_field = {\n \"name\": field_name,\n \"type\": field_type,\n \"indent\": indent,\n \"section\": current_io[\"name\"],\n \"line\": i,\n \"has_filter_from_agent\": False,\n \"filter_from_agent_value\": None,\n \"has_is_displayable\": False,\n \"is_displayable_value\": None,\n \"has_is_used_by_planner\": False,\n \"is_used_by_planner_value\": None,\n \"filter_from_agent_line\": None,\n \"is_displayable_line\": None,\n \"is_used_by_planner_line\": None,\n }\n current_action[\"io_fields\"].append(current_io_field)\n if field_type == \"date\":\n current_action[\"date_io_lines\"].append((i, field_name, current_io[\"name\"]))\n if field_name in self.RESERVED_FIELD_NAMES:\n current_action[\"reserved_io_field_lines\"].append((i, field_name, current_io[\"name\"]))\n continue\n\n if current_io_field and indent > current_io_field[\"indent\"]:\n if stripped.startswith(\"is_displayable:\"):\n value = self._clean_scalar_value(stripped.split(\":\", 1)[1].strip())\n current_io_field[\"has_is_displayable\"] = True\n current_io_field[\"is_displayable_value\"] = value\n current_io_field[\"is_displayable_line\"] = i\n if value == \"True\":\n current_action[\"prompt_output_display_lines\"].append((i, current_io_field[\"name\"]))\n if stripped.startswith(\"is_required:\") and self._clean_scalar_value(stripped.split(\":\", 1)[1].strip()) == \"True\":\n current_action[\"required_input_lines\"].append((i, current_io_field[\"name\"]))\n if stripped.startswith(\"filter_from_agent:\"):\n value = self._clean_scalar_value(stripped.split(\":\", 1)[1].strip())\n current_io_field[\"has_filter_from_agent\"] = True\n current_io_field[\"filter_from_agent_value\"] = value\n current_io_field[\"filter_from_agent_line\"] = i\n if stripped.startswith(\"is_used_by_planner:\"):\n value = self._clean_scalar_value(stripped.split(\":\", 1)[1].strip())\n current_io_field[\"has_is_used_by_planner\"] = True\n current_io_field[\"is_used_by_planner_value\"] = value\n current_io_field[\"is_used_by_planner_line\"] = i\n continue\n\n if current_action[\"kind\"] == \"utility_transition\":\n property_name = stripped.split(\":\", 1)[0].strip()\n if property_name in self.INVALID_TRANSITION_PROPERTIES:\n current_action[\"invalid_transition_properties\"].append((i, property_name))\n continue\n\n # topic/start-level descriptions/system overrides outside action contexts\n if actions_mode is None and current_action is None:\n if stripped.startswith(\"description:\"):\n desc_value = stripped.split(\":\", 1)[1].strip()\n if desc_value in {\"\", \"|\", \">\", \"|-\", \">-\", \"|+\", \">+\"}:\n self.multiline_description_issues.append((i, current_top))\n else:\n self.block_descriptions.append(\n {\n \"name\": current_block_name,\n \"kind\": current_top,\n \"line\": i,\n \"description\": self._clean_scalar_value(desc_value),\n }\n )\n continue\n\n if stripped.startswith(\"system:\") and stripped != \"system:\":\n self.topic_inline_system_lines.append((i, current_block_name or current_top))\n continue\n\n # Check last IO field for filter/planner conflict before flush\n if current_io_field and current_io_field.get(\"has_filter_from_agent\") and current_io_field.get(\"has_is_used_by_planner\") and current_action:\n current_action[\"filter_planner_conflict_lines\"].append(\n (current_io_field[\"is_used_by_planner_line\"], current_io_field[\"name\"], current_io_field[\"section\"])\n )\n self._flush_action(current_action)\n if current_connection:\n self.connection_blocks.append(current_connection)\n\n def validate(self) -> dict:\n self._check_mixed_indentation()\n self._check_boolean_case()\n self._check_required_blocks()\n self._check_config_fields()\n self._check_start_agent_count()\n self._check_name_collisions()\n self._check_naming_rules()\n self._check_invalid_connections_wrapper()\n self._check_invalid_top_level_actions()\n self._check_mutable_linked_conflict()\n self._check_reserved_variable_names()\n self._check_reserved_field_names()\n self._check_linked_variable_defaults()\n self._check_linked_variable_types()\n self._check_undefined_variables()\n self._check_undefined_topics()\n self._check_multiline_descriptions()\n self._check_topic_system_override_syntax()\n self._check_lifecycle_instruction_wrappers()\n self._check_lifecycle_pipe_content()\n self._check_lifecycle_run_portability()\n self._check_lifecycle_arithmetic_null_guard()\n self._check_post_action_position()\n self._check_empty_list_literals()\n self._check_inputs_in_set()\n self._check_bare_run_actions()\n self._check_run_resolution_to_definition_scope()\n self._check_action_metadata_context()\n self._check_target_action_io_completeness()\n self._check_multiple_available_when()\n self._check_confirmation_runtime_gap()\n self._check_prompt_output_displayability()\n self._check_prompt_hidden_outputs_need_planner()\n self._check_date_type_in_action_io()\n self._check_filter_planner_conflict()\n self._check_is_required_advisories()\n self._check_user_input_string_matching()\n self._check_string_method_portability()\n self._check_structured_output_scalar_assignment()\n self._check_invalid_else_if()\n self._check_nested_if_blocks()\n self._check_empty_conditional_bodies()\n self._check_ellipsis_misuse()\n self._check_connection_block_completeness()\n self._check_agent_type_specific_patterns()\n self._check_service_agent_user_in_org()\n self._check_apex_target_existence()\n self._check_service_agent_target_permissions()\n self._check_connection_route_flow_readiness()\n self._check_large_file_risk()\n self._check_duplicate_descriptions()\n self._check_transition_naming_conventions()\n self._check_sensitive_actions_without_guards()\n self._check_welcome_error_patterns()\n self._check_escalation_fallback_heuristic()\n self._check_platform_guardrail_topic_conflict()\n\n return {\n \"success\": len(self.errors) == 0,\n \"errors\": self.errors,\n \"warnings\": self.warnings,\n \"file_path\": self.file_path,\n \"checklist\": self._build_checklist(),\n }\n\n def _check_mixed_indentation(self):\n has_tabs = False\n has_spaces = False\n tab_line = None\n space_line = None\n\n for i, line in enumerate(self.lines, 1):\n leading = len(line) - len(line.lstrip())\n if leading \u003c= 0:\n continue\n leading_chars = line[:leading]\n if \"\\t\" in leading_chars:\n has_tabs = True\n if tab_line is None:\n tab_line = i\n if \" \" in leading_chars:\n has_spaces = True\n if space_line is None:\n space_line = i\n\n if has_tabs and has_spaces:\n self._add_error(\n tab_line or 1,\n f\"Mixed tabs and spaces detected. Tabs first seen on line {tab_line}, spaces first seen on line {space_line}. Use consistent indentation (all tabs OR all spaces).\",\n \"ASV-STR-001\",\n )\n\n def _check_boolean_case(self):\n assignment_pattern = re.compile(r\"^\\s*[A-Za-z_][A-Za-z0-9_:\\- ]*\\s*[:=]\\s*(true|false)\\s*(?:#.*)?$\", re.IGNORECASE)\n for i, line in enumerate(self.lines, 1):\n match = assignment_pattern.match(line)\n if not match:\n continue\n value = match.group(1)\n if value.lower() == \"true\" and value != \"True\":\n self._add_error(i, f\"Boolean must be capitalized: use 'True' instead of '{value}'.\", \"ASV-STR-002\")\n elif value.lower() == \"false\" and value != \"False\":\n self._add_error(i, f\"Boolean must be capitalized: use 'False' instead of '{value}'.\", \"ASV-STR-002\")\n\n def _check_required_blocks(self):\n required = {\"system\": False, \"config\": False, \"start_agent\": False}\n for line in self.lines:\n stripped = line.strip()\n if stripped.startswith(\"system:\"):\n required[\"system\"] = True\n elif stripped.startswith(\"config:\"):\n required[\"config\"] = True\n elif stripped.startswith(\"start_agent \"):\n required[\"start_agent\"] = True\n missing = [name for name, present in required.items() if not present]\n if missing:\n self._add_error(\n 1,\n f\"Missing required blocks: {', '.join(missing)}. Every agent needs config, system, and exactly one start_agent.\",\n \"ASV-STR-003\",\n )\n\n def _check_config_fields(self):\n developer_name = self.config_fields.get(\"developer_name\")\n legacy_agent_name = self.config_fields.get(\"agent_name\")\n compatibility_agent_description = self.config_fields.get(\"agent_description\")\n config_description = self.config_fields.get(\"description\")\n agent_type = self.config_fields.get(\"agent_type\")\n default_agent_user = self.config_fields.get(\"default_agent_user\")\n\n if not developer_name and not legacy_agent_name:\n self._add_error(1, \"Missing agent identifier in config block. Use either 'developer_name' or legacy 'agent_name'.\", \"ASV-CFG-001\")\n if not compatibility_agent_description and not config_description:\n self._add_error(1, \"Missing agent description in config block. Use either 'description' or compatibility 'agent_description'.\", \"ASV-CFG-001\")\n\n if compatibility_agent_description and config_description:\n self._add_warning(compatibility_agent_description[1], \"Config defines both 'description:' and 'agent_description:'. Use one field; public docs/examples prefer 'description:'.\", \"ASV-CFG-003\")\n\n for line in self.default_agent_user_comment_lines:\n self._add_warning(\n line,\n \"default_agent_user should contain only the username literal. Remove inline comments or trailing text so org-aware validation and publish-time resolution stay reliable.\",\n \"ASV-CFG-004\",\n )\n\n enable_logs = self.config_fields.get(\"enable_enhanced_event_logs\")\n if enable_logs and enable_logs[0] not in {\"True\", \"False\"}:\n self._add_warning(\n enable_logs[1],\n \"enable_enhanced_event_logs should be a boolean literal (True/False).\",\n \"ASV-CFG-005\",\n )\n\n user_locale = self.config_fields.get(\"user_locale\")\n if user_locale and not user_locale[0]:\n self._add_warning(user_locale[1], \"user_locale is present but empty. Provide a locale string or remove the field.\", \"ASV-CFG-005\")\n\n for optional_name in (\"company\", \"role\", \"agent_version\"):\n optional_field = self.config_fields.get(optional_name)\n if optional_field and not optional_field[0]:\n self._add_warning(optional_field[1], f\"{optional_name} is present but empty. Remove it or provide a string value.\", \"ASV-CFG-005\")\n\n for field_name in (\"developer_name\", \"agent_name\", \"default_agent_user\", \"agent_label\"):\n field = self.config_fields.get(field_name)\n if field and self._is_placeholder_value(field[0]):\n self._add_warning(field[1], f\"Config field '{field_name}' still contains a template placeholder ('{field[0]}'). Replace it before preview/publish.\", \"ASV-CFG-007\")\n\n if not agent_type:\n if not default_agent_user:\n self._add_error(\n 1,\n \"Missing both 'agent_type' and 'default_agent_user'. Without agent_type the compiler defaults to a Service Agent, which requires default_agent_user.\",\n \"ASV-CFG-002\",\n )\n else:\n self._add_warning(\n 1,\n \"Missing 'agent_type'. This compiles when 'default_agent_user' is present because the compiler defaults to a Service Agent, but setting agent_type explicitly is safer.\",\n \"ASV-CFG-002\",\n )\n return\n\n agent_type_value, agent_type_line = agent_type\n if agent_type_value not in {\"AgentforceServiceAgent\", \"AgentforceEmployeeAgent\"}:\n self._add_error(\n agent_type_line,\n f\"Invalid agent_type '{agent_type_value}'. Use 'AgentforceServiceAgent' or 'AgentforceEmployeeAgent'.\",\n \"ASV-CFG-002\",\n )\n return\n\n if agent_type_value == \"AgentforceServiceAgent\" and not default_agent_user:\n self._add_error(\n agent_type_line,\n \"Service Agents require 'default_agent_user' in config. Set it to a valid Einstein Agent User.\",\n \"ASV-CFG-002\",\n )\n\n if agent_type_value == \"AgentforceEmployeeAgent\" and default_agent_user:\n self._add_error(\n default_agent_user[1],\n \"Employee Agents must NOT include 'default_agent_user'. Remove it entirely; Employee Agents run as the logged-in user.\",\n \"ASV-CFG-002\",\n )\n\n def _check_start_agent_count(self):\n count = len(self.start_agent_names)\n if count == 0:\n self._add_error(1, \"Missing start_agent block. Every agent needs exactly one start_agent.\", \"ASV-STR-004\")\n elif count > 1:\n first_line = sorted(self.start_agent_names.values())[1]\n self._add_error(first_line, f\"Found {count} start_agent blocks. Exactly one start_agent is allowed.\", \"ASV-STR-004\")\n\n def _check_name_collisions(self):\n for name, line in self.start_agent_names.items():\n if name in self.topic_names:\n self._add_error(\n line,\n f\"Name collision: start_agent '{name}' has the same API name as a topic. This is a proven publish blocker because generated GenAiPluginDefinition metadata collides; use unique names.\",\n \"ASV-STR-005\",\n )\n\n def _check_name_rules(self, name: str, line: int, kind: str):\n if self._is_placeholder_value(name):\n self._add_warning(\n line,\n f\"{kind} '{name}' looks like an unresolved template placeholder. Replace it with a concrete API-safe name before publish.\",\n \"ASV-CFG-007\",\n )\n return\n if not self.NAME_PATTERN.match(name):\n self._add_error(\n line,\n f\"Invalid {kind} '{name}'. Names must start with a letter and contain only letters, numbers, and underscores (max 80 chars).\",\n \"ASV-STR-006\",\n )\n return\n if \"__\" in name:\n self._add_error(line, f\"Invalid {kind} '{name}'. Consecutive underscores are not allowed.\", \"ASV-STR-006\")\n if name.endswith(\"_\"):\n self._add_error(line, f\"Invalid {kind} '{name}'. Names cannot end with an underscore.\", \"ASV-STR-006\")\n\n def _check_naming_rules(self):\n if \"developer_name\" in self.config_fields:\n name, line = self.config_fields[\"developer_name\"]\n self._check_name_rules(name, line, \"developer_name\")\n elif \"agent_name\" in self.config_fields:\n name, line = self.config_fields[\"agent_name\"]\n self._check_name_rules(name, line, \"agent_name\")\n\n for name, line in self.topic_names.items():\n self._check_name_rules(name, line, \"topic name\")\n for name, line in self.start_agent_names.items():\n self._check_name_rules(name, line, \"start_agent name\")\n for name, line in self.variable_names.items():\n self._check_name_rules(name, line, \"variable name\")\n\n def _check_invalid_connections_wrapper(self):\n for i, line in enumerate(self.lines, 1):\n if line.strip() == \"connections:\":\n self._add_error(i, \"Invalid top-level block 'connections:'. Use 'connection messaging:' (singular) instead.\", \"ASV-STR-007\")\n\n def _check_invalid_top_level_actions(self):\n for line in self.top_level_actions_lines:\n self._add_error(line, \"Top-level 'actions:' blocks are not valid. Define actions inside a topic or start_agent block only.\", \"ASV-STR-017\")\n\n def _check_mutable_linked_conflict(self):\n pattern = re.compile(r\"mutable\\s+linked|linked\\s+mutable\", re.IGNORECASE)\n for i, line in enumerate(self.lines, 1):\n if pattern.search(line):\n self._add_error(\n i,\n \"Variable cannot be both 'mutable' AND 'linked'. Use 'mutable' for changeable state and 'linked' for external read-only data.\",\n \"ASV-STR-008\",\n )\n\n def _check_reserved_variable_names(self):\n for name, line in self.variable_names.items():\n if name in self.RESERVED_CONTEXT_VARIABLE_NAMES:\n self._add_warning(\n line,\n f\"Variable name '{name}' may conflict with platform context mappings. Prefer a prefixed name like 'customer_{name.lower()}'.\",\n \"ASV-STR-009\",\n )\n\n def _check_reserved_field_names(self):\n for variable in self.variable_definitions:\n if variable[\"name\"] in self.RESERVED_FIELD_NAMES:\n self._add_error(\n variable[\"line\"],\n f\"Variable name '{variable['name']}' is reserved in recent Agent Script / invocable parsing paths. Rename it (for example '{variable['name']}_field' or '{variable['name']}_text').\",\n \"ASV-STR-021\",\n )\n\n for action in self.action_definitions:\n for line, field_name, section in action.get(\"reserved_io_field_lines\", []):\n self._add_error(\n line,\n f\"Field name '{field_name}' in action {section} is reserved in recent Agent Script / invocable parsing paths. Rename the field to avoid parser failures.\",\n \"ASV-STR-021\",\n )\n\n def _check_linked_variable_defaults(self):\n for variable in self.variable_definitions:\n if variable[\"modifier\"] == \"linked\" and variable.get(\"default\") is not None:\n self._add_error(\n variable[\"line\"],\n f\"Linked variable '{variable['name']}' cannot declare a default value. Remove '= ...' and populate it via 'source:'.\",\n \"ASV-STR-018\",\n )\n\n def _check_linked_variable_types(self):\n for variable in self.variable_definitions:\n if variable[\"modifier\"] != \"linked\":\n continue\n var_type = variable[\"type\"]\n if var_type == \"object\" or var_type.startswith(\"list[\"):\n self._add_error(\n variable[\"line\"],\n f\"Linked variable '{variable['name']}' uses unsupported type '{var_type}'. Linked variables should use scalar types and be parsed downstream if needed.\",\n \"ASV-STR-019\",\n )\n\n def _check_undefined_variables(self):\n ref_pattern = re.compile(r\"@variables\\.([A-Za-z_][A-Za-z0-9_]*)\")\n executable_prefixes = (\"|\", \"if \", \"set \", \"run \", \"with \", \"available when\", \"transition to \")\n for i, line in enumerate(self.lines, 1):\n stripped = line.strip()\n if not stripped.startswith(executable_prefixes):\n continue\n for name in ref_pattern.findall(stripped):\n if name not in self.variable_names:\n self._add_error(i, f\"Reference to undefined variable '@variables.{name}'. Declare it in the variables block first.\", \"ASV-STR-010\")\n\n def _check_undefined_topics(self):\n ref_pattern = re.compile(r\"@topic\\.([A-Za-z][A-Za-z0-9_]*)\")\n for i, line in enumerate(self.lines, 1):\n for name in ref_pattern.findall(line):\n if name not in self.defined_topics:\n self._add_error(i, f\"Reference to undefined topic '@topic.{name}'. Define the topic or fix the reference.\", \"ASV-STR-011\")\n\n def _check_multiline_descriptions(self):\n for line, block_kind in self.multiline_description_issues:\n self._add_warning(\n line,\n f\"{block_kind} description appears multiline or block-scalar based. Keep topic/start_agent description on a single line; multiline descriptions are a known parser gotcha.\",\n \"ASV-STR-012\",\n )\n\n def _check_topic_system_override_syntax(self):\n for line, owner in self.topic_inline_system_lines:\n self._add_error(\n line,\n f\"Topic '{owner}' uses shorthand 'system: ...'. Prefer the documented nested form: 'system:' followed by 'instructions:'.\",\n \"ASV-CFG-006\",\n )\n\n def _check_lifecycle_instruction_wrappers(self):\n for line, lifecycle_name in self.lifecycle_instruction_wrappers:\n self._add_warning(\n line,\n f\"{lifecycle_name} should contain direct content, not an 'instructions:' wrapper. Put procedural lines directly under {lifecycle_name}:.\",\n \"ASV-STR-013\",\n )\n\n def _check_lifecycle_pipe_content(self):\n for line, lifecycle_name in self.lifecycle_pipe_lines:\n self._add_error(\n line,\n f\"{lifecycle_name} contains a pipe-style prompt line. Lifecycle hooks should use deterministic statements like 'set', 'if', 'run', or 'transition to' rather than '|'.\",\n \"ASV-RUN-009\",\n )\n\n def _check_lifecycle_run_portability(self):\n for line, lifecycle_name, action_name in self.lifecycle_run_lines:\n self._add_warning(\n line,\n f\"{lifecycle_name} runs '@actions.{action_name}'. Lifecycle-hook 'run' syntax is valid but not portable enough to treat as universally reliable across bundle types and org states.\",\n \"ASV-RUN-010\",\n )\n\n def _check_lifecycle_arithmetic_null_guard(self):\n # Build a set of (lifecycle_name, var_name, owner) tuples that have null guards\n guarded = set()\n for _, lifecycle_name, var_name, owner in self.lifecycle_null_guards:\n guarded.add((lifecycle_name, var_name, owner))\n\n for line, lifecycle_name, var_name, owner in self.lifecycle_arithmetic_lines:\n if (lifecycle_name, var_name, owner) not in guarded:\n var_def = self.variable_by_name.get(var_name)\n var_type = var_def[\"type\"] if var_def else \"unknown\"\n if var_type in (\"number\", \"integer\"):\n self._add_warning(\n line,\n f\"{lifecycle_name} in '{owner}' does arithmetic on '@variables.{var_name}' without a null guard. \"\n f\"Mutable number variables can be None at runtime (e.g., Eval API state injection, Testing Center). \"\n f\"Add 'if @variables.{var_name} is None: set @variables.{var_name} = 0' before the arithmetic to prevent silent crashes.\",\n \"ASV-RUN-021\",\n )\n\n def _check_post_action_position(self):\n in_instructions = False\n seen_pipe_text = False\n warned_in_block = False\n for i, line in enumerate(self.lines, 1):\n stripped = line.strip()\n if stripped.startswith(\"instructions:\"):\n in_instructions = True\n seen_pipe_text = False\n warned_in_block = False\n continue\n if in_instructions:\n if stripped.startswith((\"actions:\", \"topic \", \"start_agent \", \"before_reasoning:\", \"after_reasoning:\")):\n in_instructions = False\n warned_in_block = False\n continue\n if stripped.startswith(\"|\"):\n seen_pipe_text = True\n if not warned_in_block and seen_pipe_text and stripped.startswith(\"if \") and \"@variables.\" in stripped:\n if any(token in stripped for token in [\"_status\", \"_done\", \"_complete\", \"_processed\"]):\n self._add_warning(\n i,\n \"Post-action check appears after LLM instructions. Consider moving it to the top of instructions so it triggers on topic re-entry after action completion.\",\n \"ASV-QLT-005\",\n )\n warned_in_block = True\n\n def _check_empty_list_literals(self):\n compare_pattern = re.compile(r\"^\\s*if\\s+.*(?:==|!=)\\s*\\[\\]\\s*:?\\s*(?:#.*)?$\")\n set_pattern = re.compile(r\"^\\s*set\\s+.+?=\\s*\\[\\]\\s*(?:#.*)?$\")\n for i, line in enumerate(self.lines, 1):\n if compare_pattern.search(line):\n self._add_error(i, \"Empty list literal '[]' is not supported in expressions. Use len(@variables.list) == 0 instead.\", \"ASV-RUN-001\")\n elif set_pattern.search(line):\n self._add_error(i, \"Resetting with 'set ... = []' is a known parser gotcha. Use a temporary empty variable workaround instead.\", \"ASV-RUN-001\")\n\n def _check_inputs_in_set(self):\n pattern = re.compile(r\"^\\s*set\\s+@variables\\.[^=]+\\s*=\\s*@inputs\\.\")\n for i, line in enumerate(self.lines, 1):\n if pattern.search(line):\n self._add_warning(i, \"Using @inputs in set is a deploy-breaking anti-pattern. Capture input with @utils.setVariables or bind from @variables instead.\", \"ASV-RUN-002\")\n\n def _check_bare_run_actions(self):\n pattern = re.compile(r\"^\\s*run\\s+([A-Za-z_][A-Za-z0-9_]*)\\s*(?:#.*)?$\")\n for i, line in enumerate(self.lines, 1):\n match = pattern.search(line)\n if match:\n self._add_error(i, f\"Bare action name '{match.group(1)}' in run. Use '@actions.{match.group(1)}' explicitly.\", \"ASV-RUN-003\")\n\n def _check_run_resolution_to_definition_scope(self):\n definition_actions: Dict[str, Dict[str, Dict]] = {}\n reasoning_actions: Dict[str, Dict[str, Dict]] = {}\n for action in self.action_definitions:\n owner = action.get(\"owner\") or \"\"\n if action.get(\"scope\") == \"definition\":\n definition_actions.setdefault(owner, {})[action[\"name\"]] = action\n elif action.get(\"scope\") == \"reasoning\":\n reasoning_actions.setdefault(owner, {})[action[\"name\"]] = action\n\n pattern = re.compile(r\"^\\s*run\\s+@actions\\.([A-Za-z_][A-Za-z0-9_]*)\\b\")\n for i, line in enumerate(self.lines, 1):\n match = pattern.match(line.strip())\n if not match:\n continue\n action_name = match.group(1)\n owner = self.line_owner.get(i)\n if not owner:\n continue\n\n definition_action = definition_actions.get(owner, {}).get(action_name)\n reasoning_action = reasoning_actions.get(owner, {}).get(action_name)\n\n if definition_action:\n if not definition_action.get(\"target\"):\n kind = definition_action.get(\"kind\") or \"action\"\n self._add_error(\n i,\n f\"run @actions.{action_name} resolves to a non-target-backed '{kind}' action in '{owner}'. Deterministic 'run' only works for topic-level action definitions that declare 'target:'. Use direct 'set' / 'transition to', or let reasoning.actions invoke the utility/delegation instead.\",\n \"ASV-RUN-014\",\n )\n continue\n\n if reasoning_action:\n self._add_error(\n i,\n f\"run @actions.{action_name} resolves to a reasoning-only utility/delegation in '{owner}'. Deterministic 'run' only works for topic-level action definitions that declare 'target:'.\",\n \"ASV-RUN-014\",\n )\n continue\n\n available_targets = sorted(\n action[\"name\"]\n for action in definition_actions.get(owner, {}).values()\n if action.get(\"target\")\n )\n available_suffix = (\n f\" Available deterministic targets in '{owner}': {', '.join(available_targets)}.\"\n if available_targets\n else \"\"\n )\n self._add_error(\n i,\n f\"run @actions.{action_name} does not resolve to a topic-level target-backed action definition in '{owner}'. Deterministic 'run' requires a topic-level action with 'target:'.{available_suffix}\",\n \"ASV-RUN-014\",\n )\n\n def _check_action_metadata_context(self):\n for action in self.action_definitions:\n if action[\"kind\"] != \"utility_transition\":\n continue\n for line, property_name in action[\"invalid_transition_properties\"]:\n self._add_error(\n line,\n f\"'{property_name}' is not valid on @utils.transition actions. Use it only on target-backed action definitions with 'target:'.\",\n \"ASV-RUN-004\",\n )\n\n def _check_target_action_io_completeness(self):\n for action in self.action_definitions:\n if action.get(\"scope\") != \"definition\" or not action.get(\"target\"):\n continue\n if not action.get(\"has_outputs_block\"):\n self._add_error(\n action.get(\"target_line\") or action[\"line\"],\n f\"Target-backed action '{action['name']}' is missing an outputs: block. Server-side publish is known to fail when target actions omit outputs.\",\n \"ASV-RUN-011\",\n )\n\n def _check_multiple_available_when(self):\n for action in self.action_definitions:\n if action.get(\"available_when_count\", 0) > 1:\n self._add_warning(\n action[\"line\"],\n f\"Action '{action['name']}' declares multiple 'available when' clauses. This can be org-dependent; prefer one compound condition for portability.\",\n \"ASV-RUN-012\",\n )\n\n def _check_confirmation_runtime_gap(self):\n for action in self.action_definitions:\n for line in action.get(\"require_user_confirmation_lines\", []):\n self._add_warning(\n line,\n f\"Action '{action['name']}' uses require_user_confirmation: True. This compiles, but runtime confirmation dialogs are unreliable; prefer an explicit two-step confirmation pattern plus an available-when guard.\",\n \"ASV-RUN-013\",\n )\n\n def _check_prompt_output_displayability(self):\n for action in self.action_definitions:\n target = action.get(\"target\") or \"\"\n if not (target.startswith(\"prompt://\") or target.startswith(\"generatePromptResponse://\")):\n continue\n for line, field_name in action[\"prompt_output_display_lines\"]:\n self._add_warning(\n line,\n f\"Output '{field_name}' uses 'is_displayable: True' on a prompt target. This can cause blank agent responses; prefer 'is_displayable: False' with 'is_used_by_planner: True'.\",\n \"ASV-RUN-005\",\n )\n\n def _check_prompt_hidden_outputs_need_planner(self):\n for action in self.action_definitions:\n target = action.get(\"target\") or \"\"\n if not (target.startswith(\"prompt://\") or target.startswith(\"generatePromptResponse://\")):\n continue\n for field in action.get(\"io_fields\", []):\n if field.get(\"section\") != \"outputs\":\n continue\n\n hidden_via_display = field.get(\"has_is_displayable\") and field.get(\"is_displayable_value\") == \"False\"\n hidden_via_filter = field.get(\"has_filter_from_agent\") and field.get(\"filter_from_agent_value\") == \"True\"\n planner_visible = field.get(\"has_is_used_by_planner\") and field.get(\"is_used_by_planner_value\") == \"True\"\n\n if hidden_via_display and not planner_visible:\n self._add_warning(\n field.get(\"is_displayable_line\") or field.get(\"line\") or action.get(\"line\") or 1,\n f\"Prompt output '{field['name']}' is hidden with 'is_displayable: False' but is not marked 'is_used_by_planner: True'. If this output should influence the reply or routing, make it planner-visible as well.\",\n \"ASV-RUN-025\",\n )\n\n if hidden_via_filter and not planner_visible:\n self._add_warning(\n field.get(\"filter_from_agent_line\") or field.get(\"line\") or action.get(\"line\") or 1,\n f\"Prompt output '{field['name']}' uses 'filter_from_agent: True'. That hides the field from direct display, but prompt outputs often also need planner visibility to influence the reply. If the planner needs this value, prefer 'is_displayable: False' + 'is_used_by_planner: True' on the prompt output instead of 'filter_from_agent'.\",\n \"ASV-RUN-025\",\n )\n\n def _check_date_type_in_action_io(self):\n for action in self.action_definitions:\n for line, field_name, section in action[\"date_io_lines\"]:\n self._add_warning(\n line,\n f\"Field '{field_name}' uses 'date' in action {section}. Runtime support is unreliable; prefer 'object' with complex_data_type_name: \\\"lightning__dateType\\\" for action I/O.\",\n \"ASV-RUN-006\",\n )\n\n def _check_filter_planner_conflict(self):\n for action in self.action_definitions:\n for line, field_name, section in action.get(\"filter_planner_conflict_lines\", []):\n self._add_error(\n line,\n f\"Output '{field_name}' in action '{action['name']}' has both 'filter_from_agent' and 'is_used_by_planner'. These are mutually exclusive — remove 'is_used_by_planner' and use only 'filter_from_agent'. This causes InvalidFormatError and cascading ACTION_NOT_IN_SCOPE failures.\",\n \"ASV-RUN-020\",\n )\n\n def _check_is_required_advisories(self):\n for action in self.action_definitions:\n if action[\"has_available_when\"]:\n continue\n for line, field_name in action[\"required_input_lines\"]:\n self._add_warning(\n line,\n f\"Input '{field_name}' uses 'is_required: True', but that is only a planner hint. Add an 'available when' guard if this input must gate execution.\",\n \"ASV-RUN-007\",\n )\n\n def _check_user_input_string_matching(self):\n for i, line in enumerate(self.lines, 1):\n stripped = line.strip()\n if not (stripped.startswith(\"if \") or stripped.startswith(\"available when\")):\n continue\n if \"@system_variables.user_input\" not in stripped:\n continue\n if re.search(r\"\\b(contains|startswith|endswith)\\b\", stripped):\n self._add_warning(\n i,\n \"Raw '@system_variables.user_input' substring/prefix matching is brittle for deterministic routing. Normalize the utterance with Flow/Apex/classifier logic first, then branch on an explicit boolean or enum.\",\n \"ASV-RUN-023\",\n )\n\n def _check_string_method_portability(self):\n for i, line in enumerate(self.lines, 1):\n stripped = line.strip()\n if not (stripped.startswith(\"if \") or stripped.startswith(\"available when\")):\n continue\n if \"@system_variables.user_input\" in stripped:\n continue\n if re.search(r\"\\b(contains|startswith|endswith)\\b\", stripped):\n self._add_warning(\n i,\n \"String-method guards (`contains` / `startswith` / `endswith`) are not portable enough for control-flow-critical validation. Prefer a Flow/Apex/classifier action that returns an explicit scalar when routing or policy depends on the result.\",\n \"ASV-RUN-024\",\n )\n\n def _check_structured_output_scalar_assignment(self):\n structured_outputs_by_owner: Dict[str, Dict[str, List[Dict]]] = {}\n for action in self.action_definitions:\n if action.get(\"scope\") != \"definition\":\n continue\n owner = action.get(\"owner\") or \"\"\n for field in action.get(\"io_fields\", []):\n if field.get(\"section\") != \"outputs\":\n continue\n field_type = (field.get(\"type\") or \"\").lower()\n if field_type == \"object\" or field_type.startswith(\"list[\"):\n structured_outputs_by_owner.setdefault(owner, {}).setdefault(field.get(\"name\") or \"\", []).append(field)\n\n assignment_pattern = re.compile(\n r\"^\\s*set\\s+@variables\\.([A-Za-z_][A-Za-z0-9_]*)\\s*=\\s*@outputs\\.([A-Za-z_][A-Za-z0-9_]*)(?![\\.\\[])\",\n )\n\n for i, line in enumerate(self.lines, 1):\n match = assignment_pattern.match(line)\n if not match:\n continue\n variable_name, output_name = match.groups()\n owner = self.line_owner.get(i)\n if not owner:\n continue\n\n structured_fields = structured_outputs_by_owner.get(owner, {}).get(output_name, [])\n if not structured_fields:\n continue\n\n variable_def = self.variable_by_name.get(variable_name)\n if not variable_def:\n continue\n variable_type = (variable_def.get(\"type\") or \"\").lower()\n if variable_type == \"object\" or variable_type.startswith(\"list[\"):\n continue\n\n structured_type = structured_fields[0].get(\"type\") or \"object\"\n self._add_warning(\n i,\n f\"@outputs.{output_name} is declared as '{structured_type}' in '{owner}', but it is being assigned directly to scalar variable '@variables.{variable_name}' ({variable_type}). Do not assume structured outputs are scalars; access a concrete field (for example '@outputs.{output_name}.value' only if the schema exposes 'value') or flatten the output in Flow/Apex first.\",\n \"ASV-RUN-026\",\n )\n\n def _check_invalid_else_if(self):\n pattern = re.compile(r\"^\\s*else\\s+if\\b\")\n for i, line in enumerate(self.lines, 1):\n if pattern.search(line):\n self._add_error(i, \"'else if' is not supported. Use a compound condition or flatten the logic into sequential if blocks.\", \"ASV-STR-015\")\n\n def _check_nested_if_blocks(self):\n stack: List[Dict] = []\n conditional_start = re.compile(r\"^\\s*(if\\b.*:|else:)\\s*(?:#.*)?$\")\n for i, line in enumerate(self.lines, 1):\n stripped = line.strip()\n if not stripped or stripped.startswith(\"#\"):\n continue\n indent = self._indent(line)\n while stack and indent \u003c= stack[-1][\"indent\"]:\n stack.pop()\n if conditional_start.match(line):\n if stack and indent > stack[-1][\"indent\"]:\n self._add_error(i, \"Nested 'if' blocks are not supported in Agent Script. Use compound predicates or flatten the logic.\", \"ASV-STR-016\")\n stack.append({\"indent\": indent, \"line\": i})\n\n def _check_empty_conditional_bodies(self):\n blocks: List[Dict] = []\n conditional_start = re.compile(r\"^\\s*(if\\b.*:|else:)\\s*(?:#.*)?$\")\n for i, line in enumerate(self.lines, 1):\n stripped = line.strip()\n indent = self._indent(line)\n\n while blocks and indent \u003c= blocks[-1][\"indent\"] and stripped:\n block = blocks.pop()\n if not block[\"has_body\"]:\n self._add_error(\n block[\"line\"],\n f\"{block['kind']} block has no executable body. Add '|' text, 'set', 'run', or 'transition to' under the block.\",\n \"ASV-STR-014\",\n )\n\n if not stripped or stripped.startswith(\"#\"):\n continue\n\n if conditional_start.match(line):\n kind = \"else\" if stripped.startswith(\"else:\") else \"if\"\n blocks.append({\"line\": i, \"indent\": indent, \"kind\": kind, \"has_body\": False})\n continue\n\n for block in blocks:\n if indent > block[\"indent\"]:\n block[\"has_body\"] = True\n\n while blocks:\n block = blocks.pop()\n if not block[\"has_body\"]:\n self._add_error(\n block[\"line\"],\n f\"{block['kind']} block has no executable body. Add '|' text, 'set', 'run', or 'transition to' under the block.\",\n \"ASV-STR-014\",\n )\n\n def _check_ellipsis_misuse(self):\n valid_with_pattern = re.compile(r\"^\\s*with\\s+(?:\\\"[^\\\"]+\\\"|[A-Za-z_][A-Za-z0-9_]*)\\s*=\\s*\\.\\.\\.\\s*(?:#.*)?$\")\n invalid_pattern = re.compile(r\"(?:=|:)\\s*\\.\\.\\.\\s*(?:#.*)?$\")\n for i, line in enumerate(self.lines, 1):\n if \"...\" not in line:\n continue\n stripped = line.strip()\n if valid_with_pattern.match(stripped):\n continue\n if invalid_pattern.search(stripped):\n self._add_error(i, \"'...' is slot-filling syntax only. Use it in 'with param=...' bindings, not in variable declarations or general expressions.\", \"ASV-STR-020\")\n\n def _check_connection_block_completeness(self):\n for block in self.connection_blocks:\n channel = block.get(\"channel\")\n line = block.get(\"line\") or 1\n fields = block.get(\"fields\") or {}\n if channel not in {\"messaging\", \"voice\", \"web\"}:\n continue\n\n route_type = fields.get(\"outbound_route_type\")\n route_name = fields.get(\"outbound_route_name\")\n escalation_message = fields.get(\"escalation_message\")\n\n if route_type or route_name or escalation_message:\n missing = []\n if not route_type:\n missing.append(\"outbound_route_type\")\n if not route_name:\n missing.append(\"outbound_route_name\")\n if not escalation_message:\n missing.append(\"escalation_message\")\n if missing:\n self._add_error(\n line,\n f\"connection {channel}: uses route configuration but is missing required field(s): {', '.join(missing)}. Route properties are all-or-nothing.\",\n \"ASV-CON-001\",\n )\n\n if route_name:\n route_value, route_line = route_name\n if not route_value.startswith(\"flow://\"):\n self._add_error(\n route_line,\n f\"connection {channel}: outbound_route_name must use a flow:// target. Found '{route_value}'.\",\n \"ASV-CON-002\",\n )\n\n if route_type:\n route_type_value, route_type_line = route_type\n if route_type_value != \"OmniChannelFlow\":\n self._add_warning(\n route_type_line,\n f\"connection {channel}: outbound_route_type '{route_type_value}' is not the TDD-validated value 'OmniChannelFlow'. Verify it in the target org before publish.\",\n \"ASV-CON-003\",\n )\n\n def _check_agent_type_specific_patterns(self):\n agent_type = self._effective_agent_type()\n if agent_type == \"AgentforceEmployeeAgent\":\n for line in self.connection_messaging_lines:\n self._add_warning(\n line,\n \"Employee Agents typically should not include 'connection messaging:'. That block is generally for Service Agents / Messaging flows.\",\n \"ASV-RUN-008\",\n )\n for line in self.messaging_linked_var_lines:\n self._add_warning(\n line,\n \"Messaging-linked variables are usually unnecessary for Employee Agents and may cause configuration issues if Messaging is not set up.\",\n \"ASV-RUN-008\",\n )\n\n if agent_type == \"AgentforceServiceAgent\":\n for line in self.context_linked_var_lines:\n self._add_warning(\n line,\n \"Service Agent linked variable uses an @context.* source. These sources are not portable across NGA Service Agent configurations; verify the runtime surface carefully.\",\n \"ASV-RUN-017\",\n )\n\n def _check_service_agent_user_in_org(self):\n default_agent_user = self.config_fields.get(\"default_agent_user\")\n effective_agent_type = self._effective_agent_type()\n if effective_agent_type != \"AgentforceServiceAgent\" or not default_agent_user:\n return\n\n username, line = default_agent_user\n if self._is_demo_user_value(username):\n self._add_warning(\n line,\n f\"default_agent_user '{username}' looks like a template/demo value. Replace it with a real Einstein Agent User before preview/publish in a target org.\",\n \"ASV-CFG-007\",\n )\n return\n\n if not self.validation_org:\n self._add_warning(line, \"Could not run org-aware default_agent_user validation because no target org could be resolved.\", \"ASV-ORG-001\")\n return\n\n query_result = self._query_user_in_org(username)\n if query_result.get(\"ok\"):\n return\n\n reason = query_result.get(\"reason\")\n record = query_result.get(\"record\") or {}\n if reason == \"missing\":\n self._add_error(line, f\"Service Agent default_agent_user '{username}' was not found in org '{self.validation_org}'.\", \"ASV-ORG-001\")\n elif reason == \"inactive\":\n self._add_error(line, f\"Service Agent default_agent_user '{username}' exists in '{self.validation_org}' but IsActive is false.\", \"ASV-ORG-001\")\n elif reason == \"automated_process\":\n self._add_error(line, f\"Service Agent default_agent_user '{username}' resolves to UserType=AutomatedProcess in '{self.validation_org}'. That is not a valid Einstein Agent User for Service Agent publishing.\", \"ASV-ORG-001\")\n elif reason == \"wrong_profile\":\n profile_name = ((record.get(\"Profile\") or {}).get(\"Name\")) or \"null\"\n self._add_error(line, f\"Service Agent default_agent_user '{username}' exists in '{self.validation_org}' but Profile.Name is '{profile_name}', not 'Einstein Agent User'.\", \"ASV-ORG-001\")\n elif reason == \"query_failed\":\n detail = query_result.get(\"detail\", \"sf data query failed\")\n self._add_warning(line, f\"Could not verify default_agent_user '{username}' against org '{self.validation_org}'. sf data query failed: {detail}\", \"ASV-ORG-001\")\n\n def _check_apex_target_existence(self):\n targets = self._collect_targets()\n apex_targets = targets[\"apex\"]\n if not apex_targets or not self.validation_org:\n return\n\n query = self._query_apex_classes([name for name, _, _ in apex_targets])\n if not query.get(\"ok\"):\n detail = query.get(\"detail\", \"sf data query failed\")\n self._add_warning(1, f\"Could not verify Apex target existence in '{self.validation_org}'. sf data query failed: {detail}\", \"ASV-ORG-006\")\n return\n\n found_by_name = {record.get(\"Name\"): record for record in (query.get(\"records\") or [])}\n for apex_name, line, raw_target in apex_targets:\n if apex_name not in found_by_name:\n self._add_error(line, f\"Apex target '{raw_target}' was not found in org '{self.validation_org}'. Deploy the class before publish.\", \"ASV-ORG-006\")\n\n def _check_service_agent_target_permissions(self):\n default_agent_user = self.config_fields.get(\"default_agent_user\")\n effective_agent_type = self._effective_agent_type()\n if effective_agent_type != \"AgentforceServiceAgent\" or not default_agent_user:\n return\n\n username, user_line = default_agent_user\n if not self.validation_org:\n return\n\n user_result = self._query_user_in_org(username)\n if not user_result.get(\"ok\"):\n return\n\n targets = self._collect_targets()\n apex_targets = targets[\"apex\"]\n flow_targets = targets[\"flow\"]\n other_targets = targets[\"other\"]\n if not apex_targets and not flow_targets and not other_targets:\n return\n\n assignments = self._query_permission_set_assignments(username)\n if not assignments.get(\"ok\"):\n detail = assignments.get(\"detail\", \"sf data query failed\")\n self._add_warning(user_line, f\"Could not verify permission set assignments for Service Agent user '{username}' in '{self.validation_org}'. sf data query failed: {detail}\", \"ASV-ORG-002\")\n return\n\n assigned_names = {((record.get(\"PermissionSet\") or {}).get(\"Name\")) for record in (assignments.get(\"records\") or [])}\n assigned_names.discard(None)\n\n if not any(name == \"AgentforceServiceAgentUser\" or \"AgentforceServiceAgentUser\" in name for name in assigned_names):\n self._add_warning(\n user_line,\n f\"Service Agent user '{username}' is missing a verified AgentforceServiceAgentUser permission set/group assignment in '{self.validation_org}'. Recommend assigning it before publish to avoid runtime/publish surprises.\",\n \"ASV-ORG-002\",\n )\n\n agent_identifier = self._agent_identifier()\n custom_permset_name = f\"{agent_identifier}_Access\" if agent_identifier else None\n custom_assigned = bool(custom_permset_name and custom_permset_name in assigned_names)\n\n if apex_targets and not custom_assigned:\n first_line = apex_targets[0][1]\n self._add_warning(\n first_line,\n f\"This Service Agent uses apex:// targets but user '{username}' is not assigned custom permission set '{custom_permset_name}'. Recommend creating/assigning '{custom_permset_name}' with \u003cclassAccesses> for every Apex class target.\",\n \"ASV-ORG-002\",\n )\n\n if flow_targets and not custom_assigned:\n first_line = flow_targets[0][1]\n self._add_warning(\n first_line,\n f\"This Service Agent uses flow:// targets but user '{username}' is not assigned custom permission set '{custom_permset_name}'. Flow internals still need object/field/Apex access; verify permissions explicitly.\",\n \"ASV-ORG-002\",\n )\n\n if custom_assigned and custom_permset_name:\n permset_query = self._query_permission_set(custom_permset_name)\n permset_records = permset_query.get(\"records\") if permset_query.get(\"ok\") else []\n permset_id = permset_records[0].get(\"Id\") if permset_records else None\n\n if apex_targets and permset_id:\n apex_query = self._query_apex_classes([name for name, _, _ in apex_targets])\n apex_by_name = {record.get(\"Name\"): record for record in (apex_query.get(\"records\") or [])} if apex_query.get(\"ok\") else {}\n access_query = self._query_setup_entity_access(permset_id, [record.get(\"Id\") for record in apex_by_name.values()], \"ApexClass\") if apex_by_name else {\"ok\": True, \"records\": []}\n access_by_id = {record.get(\"SetupEntityId\"): record for record in (access_query.get(\"records\") or [])} if access_query.get(\"ok\") else {}\n for apex_name, line, raw_target in apex_targets:\n apex_record = apex_by_name.get(apex_name)\n if not apex_record:\n self._add_warning(\n line,\n f\"Could not verify Apex target '{raw_target}' in org '{self.validation_org}'. If this is namespaced or packaged, verify '{custom_permset_name}' includes explicit class access for it.\",\n \"ASV-ORG-003\",\n )\n continue\n if apex_record.get(\"Id\") not in access_by_id:\n self._add_warning(\n line,\n f\"Apex target '{raw_target}' is not granted in permission set '{custom_permset_name}'. Recommend adding \u003cclassAccesses>\u003capexClass>{apex_name}\u003c/apexClass>\u003cenabled>true\u003c/enabled>\u003c/classAccesses> and reassigning if needed.\",\n \"ASV-ORG-003\",\n )\n elif apex_targets and not permset_id:\n first_line = apex_targets[0][1]\n self._add_warning(\n first_line,\n f\"User '{username}' appears assigned to '{custom_permset_name}', but the permission set record could not be resolved in '{self.validation_org}'. Verify the permission set exists and includes class access entries.\",\n \"ASV-ORG-003\",\n )\n\n active_flows_query = self._query_active_flows([name for name, _, _ in flow_targets]) if flow_targets else {\"ok\": True, \"records\": []}\n active_flows = {record.get(\"ApiName\"): record for record in (active_flows_query.get(\"records\") or [])} if active_flows_query.get(\"ok\") else {}\n for flow_name, line, raw_target in flow_targets:\n record = active_flows.get(flow_name)\n if not active_flows_query.get(\"ok\"):\n detail = active_flows_query.get(\"detail\", \"sf data query failed\")\n self._add_warning(line, f\"Could not verify Flow target '{raw_target}' in '{self.validation_org}'. sf data query failed: {detail}\", \"ASV-ORG-004\")\n continue\n if not record:\n self._add_error(line, f\"Flow target '{raw_target}' was not found in org '{self.validation_org}'. Deploy the FlowDefinition before publish.\", \"ASV-ORG-004\")\n continue\n if not record.get(\"ActiveVersionId\"):\n self._add_error(line, f\"Flow target '{raw_target}' exists in '{self.validation_org}' but has no active version. Activate the Flow before publish.\", \"ASV-ORG-004\")\n\n for raw_target, line, _ in other_targets:\n self._add_warning(\n line,\n f\"Target '{raw_target}' uses a protocol the validator cannot permission-check automatically. Verify the Service Agent user '{username}' has all required access before publish.\",\n \"ASV-ORG-005\",\n )\n\n def _check_connection_route_flow_readiness(self):\n route_targets = self._collect_connection_route_targets()\n if not route_targets or not self.validation_org:\n return\n\n concrete_targets = []\n for flow_name, line, raw_target in route_targets:\n if self._is_placeholder_value(flow_name) or self._is_placeholder_value(raw_target):\n self._add_warning(\n line,\n f\"Connection route Flow '{raw_target}' still contains a template placeholder. Replace it with a real Flow API name before publish.\",\n \"ASV-CFG-007\",\n )\n continue\n concrete_targets.append((flow_name, line, raw_target))\n\n if not concrete_targets:\n return\n\n query = self._query_active_flows([name for name, _, _ in concrete_targets])\n if not query.get(\"ok\"):\n detail = query.get(\"detail\", \"sf data query failed\")\n self._add_warning(1, f\"Could not verify connection route Flows in '{self.validation_org}'. sf data query failed: {detail}\", \"ASV-ORG-007\")\n return\n\n flows_by_name = {record.get(\"ApiName\"): record for record in (query.get(\"records\") or [])}\n for flow_name, line, raw_target in concrete_targets:\n record = flows_by_name.get(flow_name)\n if not record:\n self._add_error(line, f\"Connection route Flow '{raw_target}' was not found in org '{self.validation_org}'. Deploy it before publish.\", \"ASV-ORG-007\")\n elif not record.get(\"ActiveVersionId\"):\n self._add_error(line, f\"Connection route Flow '{raw_target}' exists in '{self.validation_org}' but has no active version. Activate it before publish.\", \"ASV-ORG-007\")\n\n def _check_large_file_risk(self):\n if len(self.lines) > self.LARGE_FILE_LIMITS[\"lines\"] or len(self.topic_names) > self.LARGE_FILE_LIMITS[\"topics\"] or len(self.action_definitions) > self.LARGE_FILE_LIMITS[\"actions\"]:\n self._add_warning(\n 1,\n f\"Agent size is high ({len(self.lines)} lines, {len(self.topic_names)} topics, {len(self.action_definitions)} actions). Large/complex .agent files are more likely to hit parser instability or authoring drift.\",\n \"ASV-RUN-019\",\n )\n\n def _normalized_description(self, text: str) -> str:\n return re.sub(r\"\\s+\", \" \", text.strip().lower())\n\n def _check_duplicate_descriptions(self):\n topic_seen: Dict[str, Dict] = {}\n action_seen: Dict[Tuple[str, str], Dict] = {}\n\n for block in self.block_descriptions:\n normalized = self._normalized_description(block.get(\"description\") or \"\")\n if not normalized:\n continue\n if normalized in topic_seen:\n first = topic_seen[normalized]\n self._add_warning(\n block[\"line\"],\n f\"Topic/start description duplicates '{first['name']}' almost exactly. Distinct descriptions improve planner/topic selection quality.\",\n \"ASV-QLT-003\",\n )\n else:\n topic_seen[normalized] = block\n\n for action in self.action_definitions:\n description = action.get(\"description\") or \"\"\n normalized = self._normalized_description(description)\n if not normalized:\n continue\n key = (action.get(\"owner\") or \"\", normalized)\n if key in action_seen:\n first = action_seen[key]\n self._add_warning(\n action.get(\"description_line\") or action[\"line\"],\n f\"Action '{action['name']}' has a near-duplicate description of '{first['name']}' in the same block. Distinct descriptions improve action selection.\",\n \"ASV-QLT-002\",\n )\n else:\n action_seen[key] = action\n\n def _check_transition_naming_conventions(self):\n accepted_prefixes = (\"go_to_\", \"go_\", \"back\", \"begin\", \"end_\", \"new_\", \"start_\")\n for action in self.action_definitions:\n if action.get(\"kind\") != \"utility_transition\":\n continue\n if action[\"name\"].startswith(accepted_prefixes):\n continue\n self._add_warning(\n action[\"line\"],\n f\"Transition action '{action['name']}' does not use the recommended navigation naming prefixes ('go_to_', 'go_', 'back', 'start_'). Consistent naming makes routing graphs easier to read.\",\n \"ASV-QLT-001\",\n )\n\n def _check_sensitive_actions_without_guards(self):\n for action in self.action_definitions:\n if action.get(\"scope\") != \"definition\" or not action.get(\"target\"):\n continue\n haystack = f\"{action['name']} {action.get('description') or ''}\".lower()\n if any(token in haystack for token in self.SENSITIVE_ACTION_TOKENS) and not action.get(\"has_available_when\"):\n self._add_warning(\n action.get(\"description_line\") or action.get(\"target_line\") or action[\"line\"],\n f\"Sensitive action '{action['name']}' has no 'available when' guard. Consider adding deterministic gating for safety and policy control.\",\n \"ASV-QLT-004\",\n )\n\n def _check_welcome_error_patterns(self):\n for line, kind in self.welcome_error_inline_interpolation_lines:\n self._add_warning(line, f\"{kind.title()} message uses `{{!...}}` interpolation in quoted string form. For dynamic system messages, switch to template/block form with `|`.\", \"ASV-RUN-015\")\n for line, kind in self.welcome_error_folded_scalar_lines:\n self._add_warning(line, f\"{kind.title()} message uses folded scalar style (`>`). Prefer literal block style (`|`) for dynamic or multiline system messages.\", \"ASV-RUN-016\")\n\n def _check_escalation_fallback_heuristic(self):\n if \"@utils.escalate\" not in self.content or not self.connection_blocks:\n return\n fallback_tokens = [\"leave_message\", \"escalation_attempted\", \"handoff_failed\", \"human_available\", \"before_reasoning:\"]\n if not any(token in self.content for token in fallback_tokens):\n self._add_warning(\n 1,\n \"Agent uses @utils.escalate with connection routing but no obvious fallback/latch pattern was detected. Consider adding a before_reasoning guard or fallback topic to avoid escalation loops.\",\n \"ASV-RUN-018\",\n )\n\n def _check_platform_guardrail_topic_conflict(self):\n \"\"\"Warn when custom topics duplicate the 3 platform-injected guardrail tools.\n\n The platform auto-injects these tools into every topic's EnabledToolsStep:\n - Inappropriate Content Guardrail (tool name: Inappropriate_Content)\n - Prompt Injection Guardrail (tool name: Prompt_Injection)\n - Reverse Engineering Guardrail (tool name: Reverse_Engineering)\n\n Custom AgentScript topics with the same or similar names create 3+3 tool\n duplication in the LLM tool set, confusing the planner and causing\n \"unexpected error\" crashes when both the platform tool and the custom\n topic fire on the same input. The platform tools cannot be disabled.\n\n Recommended fix: Remove the custom guardrail topics and let the platform's\n built-in guardrail tools handle content moderation.\n \"\"\"\n platform_guardrail_patterns = {\n \"inappropriate\": \"Inappropriate Content Guardrail\",\n \"prompt_injection\": \"Prompt Injection Guardrail\",\n \"reverse_engineering\": \"Reverse Engineering Guardrail\",\n }\n\n conflicting_topics = []\n for topic_name, line_num in self.topic_names.items():\n topic_lower = topic_name.lower()\n for pattern, platform_name in platform_guardrail_patterns.items():\n if pattern in topic_lower:\n conflicting_topics.append((topic_name, line_num, platform_name))\n\n # Also check for transition actions in start_agent that route to guardrail topics\n guardrail_transitions = []\n for i, line in enumerate(self.lines, 1):\n stripped = line.strip()\n for pattern in platform_guardrail_patterns:\n if f\"@topic.\" in stripped and pattern in stripped.lower() and \"transition\" in stripped.lower():\n guardrail_transitions.append((i, stripped))\n\n if conflicting_topics:\n topic_list = \", \".join(f\"'{t[0]}' (line {t[1]}, conflicts with {t[2]})\" for t in conflicting_topics)\n self._add_warning(\n conflicting_topics[0][1],\n f\"Custom guardrail topic(s) {topic_list} duplicate the platform's built-in guardrail tools. \"\n f\"The platform auto-injects 'Inappropriate Content Guardrail', 'Prompt Injection Guardrail', and \"\n f\"'Reverse Engineering Guardrail' into every topic's tool set. Custom topics with similar names create \"\n f\"3+3 tool duplication, confusing the LLM planner and causing 'unexpected error' crashes. \"\n f\"Consider removing custom guardrail topics and relying on the platform's built-in guardrail tools.\",\n \"ASV-RUN-022\",\n )\n\n def _issue_texts(self, severity: str) -> List[str]:\n issues = self.errors if severity == \"error\" else self.warnings\n return [message for _, _, message in issues]\n\n def _checklist_entry(self, section: str, label: str, error_terms: List[str], warning_terms: Optional[List[str]] = None, success_detail: str = \"No issues found.\", na_detail: Optional[str] = None, applicable: bool = True, confidence: Optional[str] = None) -> Dict[str, str]:\n if not applicable:\n return {\"section\": section, \"status\": \"info\", \"icon\": \"ℹ️\", \"label\": label, \"detail\": na_detail or \"Not applicable.\", \"confidence\": confidence or \"Informational\"}\n\n warning_terms = warning_terms or error_terms\n error_matches = [msg for msg in self._issue_texts(\"error\") if any(term in msg for term in error_terms)]\n if error_matches:\n return {\"section\": section, \"status\": \"error\", \"icon\": \"❌\", \"label\": label, \"detail\": error_matches[0], \"confidence\": confidence or \"Blocking\"}\n\n warning_matches = [msg for msg in self._issue_texts(\"warning\") if any(term in msg for term in warning_terms)]\n if warning_matches:\n return {\"section\": section, \"status\": \"warning\", \"icon\": \"⚠️\", \"label\": label, \"detail\": warning_matches[0], \"confidence\": confidence or \"Warning\"}\n\n return {\"section\": section, \"status\": \"ok\", \"icon\": \"✅\", \"label\": label, \"detail\": success_detail, \"confidence\": confidence or \"Check\"}\n\n def _build_checklist(self) -> List[Dict[str, str]]:\n effective_agent_type = self._effective_agent_type()\n targets = self._collect_targets()\n route_targets = self._collect_connection_route_targets()\n has_targets = bool(targets[\"apex\"] or targets[\"flow\"] or targets[\"other\"])\n service_agent = effective_agent_type == \"AgentforceServiceAgent\"\n employee_agent = effective_agent_type == \"AgentforceEmployeeAgent\"\n prompt_targets_present = any((action.get(\"target\") or \"\").startswith((\"prompt://\", \"generatePromptResponse://\")) for action in self.action_definitions)\n\n return [\n self._checklist_entry(\"Structure\", \"Indentation consistency\", [\"ASV-STR-001\"], success_detail=\"Tabs/spaces usage is consistent.\", confidence=\"Compiler rule\"),\n self._checklist_entry(\"Structure\", \"Boolean capitalization\", [\"ASV-STR-002\"], success_detail=\"Boolean literals use True/False correctly.\", confidence=\"Compiler rule\"),\n self._checklist_entry(\"Structure\", \"Required top-level blocks\", [\"ASV-STR-003\"], success_detail=\"config, system, and start_agent blocks are present.\", confidence=\"Compiler rule\"),\n self._checklist_entry(\"Structure\", \"Exactly one start_agent\", [\"ASV-STR-004\"], success_detail=\"Exactly one start_agent block is defined.\", confidence=\"Compiler rule\"),\n self._checklist_entry(\"Structure\", \"Topic/start_agent naming collisions\", [\"ASV-STR-005\"], success_detail=\"No topic/start_agent API-name collisions detected.\", confidence=\"Proven publish blocker\"),\n self._checklist_entry(\"Structure\", \"Naming rule compliance\", [\"ASV-STR-006\"], success_detail=\"Names follow Agent Script naming rules.\"),\n self._checklist_entry(\"Structure\", \"Connections/actions top-level blocks\", [\"ASV-STR-007\", \"ASV-STR-017\"], success_detail=\"No invalid top-level connections/actions wrappers detected.\"),\n self._checklist_entry(\"Structure\", \"Variable and field safety\", [\"ASV-STR-008\", \"ASV-STR-009\", \"ASV-STR-010\", \"ASV-STR-018\", \"ASV-STR-019\", \"ASV-STR-020\", \"ASV-STR-021\"], success_detail=\"Variable declarations, field names, and executable references look consistent.\"),\n self._checklist_entry(\"Structure\", \"Topic references\", [\"ASV-STR-011\"], success_detail=\"All @topic references resolve to defined topics.\"),\n self._checklist_entry(\"Structure\", \"Description formatting\", [\"ASV-STR-012\"], success_detail=\"Topic/start_agent descriptions stay on a single safe line.\"),\n self._checklist_entry(\"Structure\", \"Conditional block structure\", [\"ASV-STR-014\", \"ASV-STR-015\", \"ASV-STR-016\"], success_detail=\"Conditional blocks avoid unsupported else-if/nested/empty-body patterns.\"),\n self._checklist_entry(\"Structure\", \"Lifecycle hook formatting\", [\"ASV-STR-013\", \"ASV-RUN-009\", \"ASV-RUN-010\"], success_detail=\"Lifecycle hooks use direct deterministic content.\"),\n self._checklist_entry(\"Structure\", \"Lifecycle arithmetic null guards\", [\"ASV-RUN-021\"], success_detail=\"Lifecycle arithmetic operations have null guards.\", na_detail=\"No lifecycle arithmetic operations detected.\", applicable=bool(self.lifecycle_arithmetic_lines), confidence=\"Runtime crash risk\"),\n self._checklist_entry(\"Agent identity\", \"Config field completeness\", [\"ASV-CFG-001\", \"ASV-CFG-003\", \"ASV-CFG-004\", \"ASV-CFG-005\", \"ASV-CFG-007\"], success_detail=\"Config fields are present and shaped safely.\", confidence=\"Compiler / authoring rule\"),\n self._checklist_entry(\"Agent identity\", \"Service vs Employee agent semantics\", [\"ASV-CFG-002\"], success_detail=\"Agent type and default_agent_user relationship is valid.\", confidence=\"Compiler / platform rule\"),\n self._checklist_entry(\"Agent identity\", \"Topic system override syntax\", [\"ASV-CFG-006\"], success_detail=\"Topic-level system overrides use the documented nested form.\", na_detail=\"No topic-level system override shorthand detected.\", applicable=bool(self.topic_inline_system_lines), confidence=\"Docs alignment / future-proofing\"),\n self._checklist_entry(\"Connections\", \"Connection routing completeness\", [\"ASV-CON-001\", \"ASV-CON-002\", \"ASV-CON-003\"], success_detail=\"Connection routing fields are complete and use validated shapes.\", na_detail=\"No connection routing blocks detected.\", applicable=bool(self.connection_blocks), confidence=\"Publish / runtime rule\"),\n self._checklist_entry(\"Targets & permissions\", \"Service Agent user exists in target org\", [\"ASV-ORG-001\"], success_detail=\"default_agent_user resolves to an active Einstein Agent User.\", na_detail=\"Not applicable to Employee Agents or agents without default_agent_user.\", applicable=service_agent and bool(self.config_fields.get(\"default_agent_user\")), confidence=\"Configuration drift / publish risk\"),\n self._checklist_entry(\"Targets & permissions\", \"Apex target existence\", [\"ASV-ORG-006\"], success_detail=\"All apex:// targets exist in the resolved org.\", na_detail=\"No apex:// targets detected.\", applicable=bool(targets[\"apex\"]), confidence=\"Publish rule\"),\n self._checklist_entry(\"Targets & permissions\", \"Required Service Agent permission assignments\", [\"ASV-ORG-002\"], success_detail=\"Required system/custom permission assignments are present for the detected targets.\", na_detail=\"No Service Agent target-backed actions require permission assignment checks.\", applicable=service_agent and has_targets, confidence=\"Likely runtime risk\"),\n self._checklist_entry(\"Targets & permissions\", \"Apex target permission coverage\", [\"ASV-ORG-003\"], success_detail=\"All apex:// targets are covered by the assigned custom permission set.\", na_detail=\"No apex:// targets detected.\", applicable=service_agent and bool(targets[\"apex\"]), confidence=\"Likely runtime risk\"),\n self._checklist_entry(\"Targets & permissions\", \"Flow target readiness\", [\"ASV-ORG-004\"], success_detail=\"All flow:// targets exist and have an active version.\", na_detail=\"No flow:// targets detected.\", applicable=bool(targets[\"flow\"]), confidence=\"Proven publish blocker\"),\n self._checklist_entry(\"Targets & permissions\", \"Connection route flow readiness\", [\"ASV-ORG-007\"], success_detail=\"All connection route Flows exist and have an active version.\", na_detail=\"No outbound connection route Flows detected.\", applicable=bool(route_targets), confidence=\"Proven publish blocker\"),\n self._checklist_entry(\"Targets & permissions\", \"Other target protocol review\", [\"ASV-ORG-005\"], success_detail=\"All detected target protocols are fully supported by automatic checks.\", na_detail=\"No non-apex/non-flow targets detected.\", applicable=bool(targets[\"other\"]), confidence=\"Manual review\"),\n self._checklist_entry(\"Runtime gotchas\", \"Collection / set gotchas\", [\"ASV-RUN-001\", \"ASV-RUN-002\"], success_detail=\"No known collection/set gotchas detected.\", confidence=\"Compiler / deploy rule\"),\n self._checklist_entry(\"Runtime gotchas\", \"Action invocation syntax\", [\"ASV-RUN-003\", \"ASV-RUN-014\"], success_detail=\"run statements resolve only to topic-level target-backed @actions definitions.\", confidence=\"Compiler / resolution rule\"),\n self._checklist_entry(\"Runtime gotchas\", \"Utility action metadata\", [\"ASV-RUN-004\"], success_detail=\"@utils.transition metadata usage is valid.\", confidence=\"Compiler rule\"),\n self._checklist_entry(\"Runtime gotchas\", \"Target action schema completeness\", [\"ASV-RUN-011\"], success_detail=\"Target-backed actions declare outputs: blocks for publish safety.\", na_detail=\"No target-backed action definitions detected.\", applicable=has_targets, confidence=\"Publish blocker\"),\n self._checklist_entry(\"Runtime gotchas\", \"Multiple available-when portability\", [\"ASV-RUN-012\"], success_detail=\"Actions avoid duplicate available-when clauses.\", na_detail=\"No actions detected.\", applicable=bool(self.action_definitions), confidence=\"Org-dependent behavior\"),\n self._checklist_entry(\"Runtime gotchas\", \"Confirmation dialog reliability\", [\"ASV-RUN-013\"], success_detail=\"No risky require_user_confirmation usage detected.\", na_detail=\"No target-backed action definitions detected.\", applicable=bool(self.action_definitions), confidence=\"Runtime gap\"),\n self._checklist_entry(\"Runtime gotchas\", \"Prompt output displayability\", [\"ASV-RUN-005\", \"ASV-RUN-025\"], success_detail=\"Prompt outputs use safe display/planner visibility settings.\", na_detail=\"No prompt targets detected.\", applicable=prompt_targets_present, confidence=\"Runtime gotcha\"),\n self._checklist_entry(\"Runtime gotchas\", \"Action I/O date typing\", [\"ASV-RUN-006\"], success_detail=\"No risky 'date' action I/O declarations detected.\", na_detail=\"No action I/O declarations detected.\", applicable=bool(self.action_definitions), confidence=\"Runtime gotcha\"),\n self._checklist_entry(\"Runtime gotchas\", \"Output visibility conflict\", [\"ASV-RUN-020\"], success_detail=\"No filter_from_agent + is_used_by_planner conflicts detected.\", na_detail=\"No action output declarations detected.\", applicable=bool(self.action_definitions), confidence=\"Blocking / cascade risk\"),\n self._checklist_entry(\"Runtime gotchas\", \"Planner-required input hints\", [\"ASV-RUN-007\"], success_detail=\"Required inputs are either guarded or not using the risky planner-only hint.\", na_detail=\"No action inputs detected.\", applicable=bool(self.action_definitions), confidence=\"Planner behavior\"),\n self._checklist_entry(\"Runtime gotchas\", \"Deterministic string-guard portability\", [\"ASV-RUN-023\", \"ASV-RUN-024\"], success_detail=\"No brittle raw-user-input or string-method guards detected.\", confidence=\"Portability warning\"),\n self._checklist_entry(\"Runtime gotchas\", \"Structured output access\", [\"ASV-RUN-026\"], success_detail=\"Structured outputs are not being assigned directly into scalar variables.\", na_detail=\"No structured-output direct assignments detected.\", applicable=bool(self.action_definitions), confidence=\"Schema / runtime warning\"),\n self._checklist_entry(\"Runtime gotchas\", \"Agent-type-specific messaging/context patterns\", [\"ASV-RUN-008\", \"ASV-RUN-017\"], success_detail=\"No agent-type-specific Messaging/context issues detected.\", na_detail=\"No employee/service-agent-only Messaging/context checks apply.\", applicable=employee_agent or service_agent, confidence=\"Runtime / portability\"),\n self._checklist_entry(\"Runtime gotchas\", \"Welcome/error message stability\", [\"ASV-RUN-015\", \"ASV-RUN-016\"], success_detail=\"System welcome/error messages avoid inline interpolation and folded-scalar pitfalls.\", na_detail=\"No obvious welcome/error message patterns detected.\", applicable=bool(self.welcome_error_inline_interpolation_lines or self.welcome_error_folded_scalar_lines), confidence=\"Authoring guidance\"),\n self._checklist_entry(\"Runtime gotchas\", \"Escalation fallback resilience\", [\"ASV-RUN-018\"], success_detail=\"Escalation usage includes an obvious fallback or latch pattern.\", na_detail=\"No @utils.escalate usage detected.\", applicable=\"@utils.escalate\" in self.content, confidence=\"Heuristic warning\"),\n self._checklist_entry(\"Runtime gotchas\", \"Large file parser risk\", [\"ASV-RUN-019\"], success_detail=\"File size/complexity stays below the configured parser-risk thresholds.\", confidence=\"Heuristic warning\"),\n self._checklist_entry(\"Runtime gotchas\", \"Platform guardrail topic conflict\", [\"ASV-RUN-022\"], success_detail=\"No custom topics duplicate the platform's built-in guardrail tools.\", na_detail=\"No guardrail-related topic names detected.\", applicable=any(p in n.lower() for n in self.topic_names for p in (\"inappropriate\", \"prompt_injection\", \"reverse_engineering\")), confidence=\"Runtime crash risk\"),\n self._checklist_entry(\"Quality\", \"Transition naming conventions\", [\"ASV-QLT-001\"], success_detail=\"Transition actions follow the recommended go_to_ naming pattern.\", na_detail=\"No utility transitions detected.\", applicable=any(action.get(\"kind\") == \"utility_transition\" for action in self.action_definitions), confidence=\"Style / maintainability\"),\n self._checklist_entry(\"Quality\", \"Action description distinctness\", [\"ASV-QLT-002\"], success_detail=\"Action descriptions are distinct enough to guide planner selection.\", na_detail=\"No repeated action descriptions detected.\", applicable=bool(self.action_definitions), confidence=\"LLM routing quality\"),\n self._checklist_entry(\"Quality\", \"Topic description distinctness\", [\"ASV-QLT-003\"], success_detail=\"Topic descriptions are distinct enough to guide topic selection.\", na_detail=\"No repeated topic/start descriptions detected.\", applicable=bool(self.block_descriptions), confidence=\"LLM routing quality\"),\n self._checklist_entry(\"Quality\", \"Sensitive action guard suggestions\", [\"ASV-QLT-004\"], success_detail=\"Sensitive actions use deterministic gating or avoid risky verbs.\", na_detail=\"No obviously sensitive target-backed actions detected.\", applicable=bool(self.action_definitions), confidence=\"Best practice\"),\n ]\n\n\ndef format_output(result: dict) -> str:\n lines = []\n file_name = Path(result[\"file_path\"]).name\n checklist = result.get(\"checklist\") or []\n\n if result[\"success\"]:\n lines.append(f\"✅ Agent Script Validation Passed: {file_name}\")\n else:\n lines.append(f\"❌ Agent Script validation found blocking issues in {file_name}\")\n\n error_count = sum(1 for item in checklist if item.get(\"status\") == \"error\")\n warning_count = sum(1 for item in checklist if item.get(\"status\") == \"warning\")\n info_count = sum(1 for item in checklist if item.get(\"status\") == \"info\")\n ok_count = sum(1 for item in checklist if item.get(\"status\") == \"ok\")\n\n lines.append(f\"📊 Summary: {error_count} blocking, {warning_count} warnings, {info_count} informational, {ok_count} passing checks\")\n\n if result[\"errors\"]:\n recommendation = \"Fix blocking issues before preview/publish.\"\n elif result[\"warnings\"]:\n recommendation = \"Safe to preview. Review warnings before publish.\"\n else:\n recommendation = \"Safe to preview and publish.\"\n\n lines.append(f\"➡️ Recommended next step: {recommendation}\")\n lines.append(\"\")\n lines.append(\"Validation checklist\")\n lines.append(\"--------------------\")\n\n current_section = None\n for item in checklist:\n section = item.get(\"section\", \"General\")\n if section != current_section:\n if current_section is not None:\n lines.append(\"\")\n lines.append(f\"{section}:\")\n current_section = section\n confidence = item.get(\"confidence\")\n confidence_text = f\" [{confidence}]\" if confidence else \"\"\n lines.append(f\" {item['icon']} {item['label']}{confidence_text} — {item['detail']}\")\n\n if result[\"errors\"]:\n lines.append(\"\")\n lines.append(\"Blocking issues\")\n lines.append(\"---------------\")\n for line_num, _, message in result[\"errors\"]:\n lines.append(f\"❌ Line {line_num}: {message}\")\n lines.append(\"\")\n lines.append(\"Fix the blocking issues above before validate/preview/publish.\")\n\n if result[\"warnings\"]:\n lines.append(\"\")\n lines.append(\"Warnings\")\n lines.append(\"--------\")\n for line_num, _, message in result[\"warnings\"]:\n lines.append(f\"⚠️ Line {line_num}: {message}\")\n\n return \"\\n\".join(lines)\n\n\ndef main():\n try:\n hook_input = json.load(sys.stdin)\n except json.JSONDecodeError:\n sys.exit(0)\n\n tool_input = hook_input.get(\"tool_input\", {})\n file_path = tool_input.get(\"file_path\", \"\")\n\n if not file_path.endswith(\".agent\"):\n sys.exit(0)\n if not os.path.exists(file_path):\n sys.exit(0)\n\n try:\n with open(file_path, \"r\", encoding=\"utf-8\") as handle:\n content = handle.read()\n except Exception as exc:\n print(f\"⚠️ Could not read {file_path}: {exc}\")\n sys.exit(0)\n\n validator = AgentScriptValidator(content, file_path)\n result = validator.validate()\n output = format_output(result)\n if output:\n print(output)\n\n sys.exit(0)\n\n\nif __name__ == \"__main__\":\n main()\n","content_type":"text/x-python; charset=utf-8","language":"python","size":118330,"content_sha256":"ebad066257d372709bfc5ff33d962973f72eba0eef79b22792531c2031f8b623"},{"filename":"README.md","content":"# sf-ai-agentscript\n\n🤖 **Agent Script DSL development for Salesforce Agentforce** - Write deterministic agents in a single `.agent` file with FSM architecture, instruction resolution, and hybrid reasoning.\n\n> Default workflow: direct `.agent` authoring first. Spec generation and scaffolds are optional helpers, not the primary authoring model.\n\n## Features\n\n- ✅ **100-point scoring system** across 6 categories for quality assurance\n- ✅ **Complete syntax reference** for Agent Script DSL\n- ✅ **FSM architecture patterns** - Hub-and-spoke, verification gates, escalation chains\n- ✅ **Instruction resolution guidance** - Three-phase execution model\n- ✅ **Debugging support** - Trace analysis and forensic debugging\n- ✅ **CLI deployment** - Retrieve, validate, deploy via `sf agent` commands\n- ✅ **Cross-skill integration** - Works with sf-flow, sf-ai-agentforce-testing, sf-deploy\n\n## Requirements\n\n| Requirement | Value |\n|-------------|-------|\n| API Version | 66.0+ |\n| License | Agentforce |\n| Einstein Agent User | Required for Service Agents only |\n| SF CLI | v2+ with agent commands |\n\n## Installation\n\n```bash\n# Install as part of sf-skills\nnpx skills add Jaganpro/sf-skills\n\n# Or install just this skill\nnpx skills add Jaganpro/sf-skills --skill sf-ai-agentscript\n```\n\n## Quick Start\n\n### 1. Invoke the Skill\n```\n/sf-ai-agentscript\n```\n\n### 2. Describe Your Agent\nTell Claude what your agent should do, and it will generate a scored Agent Script.\n\n### 3. Validate & Deploy\n```bash\nsf org create agent-user --target-org TARGET_ORG --json # Service Agents only\nsf agent validate authoring-bundle --api-name MyAgent -o TARGET_ORG --json\nsf agent publish authoring-bundle --api-name MyAgent --target-org prod --json\n```\n\n## Scoring System\n\n| Category | Points | Focus |\n|----------|--------|-------|\n| Structure & Syntax | 20 | Required blocks, indentation, required fields |\n| Deterministic Logic | 25 | Security guards, post-action checks |\n| Instruction Resolution | 20 | Arrow syntax, template injection |\n| FSM Architecture | 15 | Topic separation, transitions |\n| Action Configuration | 10 | Protocols, I/O mapping |\n| Deployment Readiness | 10 | Valid user, clean validation |\n\n**Thresholds:** 90+ Excellent | 80-89 Very Good | 70-79 Good | 60-69 Needs Work | \u003c60 Critical\n\n## Key Rules\n\n1. **`default_agent_user` MUST exist (Service Agents)** - Not needed for Employee Agents. See [references/agent-user-setup.md](references/agent-user-setup.md)\n2. **No mixed tabs/spaces** - Use consistent indentation throughout\n3. **Booleans are `True`/`False`** - Python-style, not JavaScript\n4. **Exactly one `start_agent`** - Single entry point required\n5. **Use `available when` for security** - Don't rely on prompts\n6. **Do not branch directly on raw `@system_variables.user_input` substring checks** - Normalize intent first\n7. **Prompt template outputs should usually be `is_displayable: False` + `is_used_by_planner: True`** - Avoid blank responses\n8. **Dynamic `system.messages.welcome` / `error` should use `|`** - Quoted strings are fine for static text, but if a system message contains `{!...}` interpolation, switch that message to template/block form\n9. **Employee Agents do not need a dedicated running user** - They need end-user visibility via permission sets when surfaced in Lightning Experience\n\n## Quick Syntax Reference\n\nCommon minimal layout (`connection:` and `knowledge:` omitted here because they are optional):\n\n```yaml\n# Block structure\nconfig: # Required: Agent metadata\nvariables: # Optional: State management\nsystem: # Required: Messages and instructions\nlanguage: # Optional: Locale settings\nstart_agent: # Required: Entry point\ntopic: # Required: Conversation handlers\n\n# Instruction patterns\ninstructions: -> # Arrow syntax for expressions\n if @variables.verified: # Conditional (resolves before LLM)\n | Welcome back! # Literal text for LLM\n run @actions.load_data # Execute a topic-level target-backed action deterministically\n set @var = @outputs.val # Capture output\n\n# Action guards\nactions:\n process: @actions.refund\n available when @variables.verified == True # LLM can't see if False\n\n# System message patterns\nsystem:\n messages:\n welcome: \"Hello!\" # Static text: quotes are fine\n # welcome: | # Dynamic text with {!...}: use block form\n # Hi {[email protected]_name}!\n```\n\n## Documentation\n\n| Document | Description |\n|----------|-------------|\n| [SKILL.md](SKILL.md) | Main entry point with scoring system |\n| [references/](references/) | Comprehensive guides per topic |\n| [references/](references/) | Quick reference guides |\n| [assets/](assets/) | Example `.agent` files and starter scaffolds |\n\n## Cross-Skill Workflow\n\n```\n/sf-flow → /sf-ai-agentscript → /sf-ai-agentforce-testing → /sf-deploy\n ↑ ↑ ↑ ↑\nCreate Flows Write agent Test routing Deploy to org\n```\n\n## Official Resources\n\n- [Agent Script Documentation](https://developer.salesforce.com/docs/ai/agentforce/guide/agent-script.html)\n- [Agentforce Builder Guide](https://help.salesforce.com/s/articleView?id=sf.copilot_builder_overview.htm)\n- [Agentforce DX Guide](https://developer.salesforce.com/docs/ai/agentforce/guide/agent-dx.html)\n\n## License\n\nMIT License - See [LICENSE](LICENSE)\n\n---\n\nCreated by [Jag Valaiyapathy](https://github.com/Jaganpro)\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":5482,"content_sha256":"b1fcd0c36619ff8afb935c64009ed2becc808d77da1f5b1565a69f0231227e0d"},{"filename":"references/action-patterns.md","content":"\u003c!-- Parent: sf-ai-agentscript/SKILL.md -->\n# Advanced Action Patterns\n\n> Context-aware descriptions, instruction references, and binding strategies\n\n**Related**: [SKILL.md — Action Chaining](../SKILL.md) | [action-prompt-templates.md](action-prompt-templates.md)\n\n---\n\n## 1. Context-Aware Action Descriptions\n\nThe same underlying action can have **different descriptions** across topics. This improves LLM action selection by matching the description to the user's expertise level or business context.\n\n### Beginner vs. Advanced Mode\n\n```agentscript\ntopic beginner_help:\n reasoning:\n actions:\n search_help: @actions.search_knowledge_base\n description: \"Search for help articles\"\n\ntopic advanced_help:\n reasoning:\n actions:\n search_help: @actions.search_knowledge_base\n description: \"Execute knowledge base query with advanced filters (type, date range, tags). Returns paginated results with relevance scoring. Supports Boolean operators and wildcards.\"\n```\n\n**Result**: Beginner users see simple language; advanced users see technical capabilities.\n\n### `available when` + Description Override\n\nCombine conditional availability with contextual descriptions for stronger control:\n\n```agentscript\ntopic business_hours:\n reasoning:\n actions:\n create_case: @actions.create_support_case\n available when @variables.during_business_hours == True\n description: \"Create a support case for immediate assignment to available agents\"\n\ntopic after_hours:\n reasoning:\n actions:\n create_case: @actions.create_support_case\n available when @variables.during_business_hours == False\n description: \"Create a support case for next-business-day follow-up\"\n```\n\n### When to use description overrides\n\n| Scenario | Use Override? |\n|----------|--------------|\n| Same action serves different user expertise levels | ✅ Yes |\n| Same action has different context in different topics | ✅ Yes |\n| Action is always used the same way | ❌ No — single description suffices |\n| Action name alone is already clear | ❌ No — don't over-engineer |\n\n---\n\n## 2. Instruction Action References\n\nThe `{[email protected]_name}` syntax embeds a **reference to the full action definition** (not just the name) into reasoning instructions. This gives the LLM richer context about the action's inputs, outputs, and purpose — improving selection accuracy when multiple actions could apply.\n\n### Basic Syntax\n\n```agentscript\ntopic business_hours_routing:\n data:\n @variables.next_open_time: ...\n\n reasoning:\n instructions: ->\n if @variables.next_open_time:\n | We are currently OUTSIDE business hours.\n The next available time is {[email protected]_open_time}.\n Create a support case using {[email protected]_case} to ensure\n follow-up during business hours.\n else:\n | We are currently WITHIN business hours.\n Connect the customer to live support using {[email protected]_to_agent}.\n```\n\n### Conditional Instruction Patterns\n\nGuide the LLM toward specific actions based on state:\n\n```agentscript\ntopic identity_verification:\n reasoning:\n instructions: ->\n if @variables.verification_attempts \u003c 3:\n | Verify identity using {[email protected]_email_code}\n or {[email protected]_phone_code}.\n else:\n | Maximum attempts reached. Escalate using {[email protected]_to_security}.\n```\n\n```agentscript\ntopic order_inquiry:\n reasoning:\n instructions: ->\n if @variables.order_status == \"shipped\":\n | Track shipment using {[email protected]_tracking_info}.\n if @variables.order_status == \"processing\":\n | Check status using {[email protected]_fulfillment_status}.\n if @variables.order_status == \"cancelled\":\n | Process refund using {[email protected]_refund}.\n```\n\n### Combining with `available when`\n\nFor maximum control, use both instruction references AND deterministic guards:\n\n```agentscript\ntopic secure_operations:\n reasoning:\n instructions: ->\n if @variables.verified == True:\n | You can now access sensitive operations using {[email protected]_account_details}.\n else:\n | Please verify your identity first.\n\n actions:\n view_account_details: @actions.get_account_info\n available when @variables.verified == True\n description: \"View sensitive account information\"\n```\n\n**Result**: Instruction guidance steers the LLM, while `available when` provides a deterministic safety net.\n\n### Descriptions vs. References: When to Use Which\n\n| Approach | When to Use |\n|----------|-------------|\n| **Description overrides** | Action needs different descriptions per topic/context |\n| **Instruction references** | Need to explicitly guide LLM toward an action in instructions |\n| **Both** | Maximum control — override description AND reference in instructions |\n\n---\n\n## 3. Input Binding Decision Matrix\n\nAgent Script supports four input binding approaches. Use this matrix to choose:\n\n| Binding | Syntax | Use When | Example |\n|---------|--------|----------|---------|\n| **LLM slot-filling** | `...` | User provides the value in conversation | `with query=...` |\n| **Variable binding** | `@variables.X` | Data exists from prior turns or actions | `with [email protected]_id` |\n| **Fixed value** | literal | System constant or business rule | `with format=\"pdf\"` |\n| **Mixed** | combination | Complex actions needing multiple sources | See below |\n\n### Mixed Binding Example\n\n```agentscript\nprocess_refund: @actions.process_refund_request\n with [email protected]_id # Variable — from previous action\n reason=... # Slot-fill — user explains why\n [email protected]_total # Variable — stored value\n refund_method=\"original_payment\" # Fixed — business rule\n require_approval=True # Fixed — policy constant\n```\n\n### Combined Pattern: Capture → Reuse → Extend\n\n```agentscript\n# Step 1: Capture data from first action\nsearch: @actions.search_products\n with query=...\n set @variables.product_id = @outputs.top_result_id\n\n# Step 2: Reuse captured value + collect new input\nadd_to_cart: @actions.add_to_cart\n with [email protected]_id # Reuse from step 1\n quantity=... # New user input\n apply_discount=True # Business rule\n```\n\n---\n\n## 4. Callback Behavior Notes\n\nThese supplement the \"Action Chaining with `run` Keyword\" section in SKILL.md.\n\n### Callbacks only execute if the parent action succeeds\n\n```agentscript\nverify_payment: @actions.verify_payment_method\n with payment_method_id=...\n set @variables.verified = @outputs.success\n run @actions.process_payment # Only runs if verify succeeds\n with payment_method_id=...\n```\n\nIf `verify_payment_method` fails (Flow error, Apex exception), `process_payment` **will not execute**. Design your conversation flow to handle the failure case separately.\n\n### Flatten, don't nest\n\n```agentscript\n# ❌ WRONG — nested callbacks (not allowed)\naction1: @actions.first\n run @actions.second\n run @actions.third # ERROR: Nested callbacks not supported\n\n# ✅ CORRECT — sequential callbacks (flat structure)\naction1: @actions.first\n run @actions.second\n run @actions.third\n```\n\n### Practical callback chain: Create → Notify → Log\n\n```agentscript\ncreate_case: @actions.create_support_case\n with subject=...\n description=...\n [email protected]\n set @variables.case_id = @outputs.id\n run @actions.notify_team\n with [email protected]_id\n [email protected]\n run @actions.log_case_creation\n with [email protected]_id\n```\n\n---\n\n## 5. Additional Error Patterns\n\nThese supplement the \"Common Issues\" table in SKILL.md.\n\n| Error | Cause | Fix |\n|-------|-------|-----|\n| Missing colon after action name | `my_action` instead of `my_action:` | Add colon: `my_action:` |\n| Missing type annotation on input | `email:` with no type | Add type: `email: string` |\n| Wrong target protocol | `flows://MyFlow` | Use `flow://MyFlow` (no trailing `s`) |\n| `Input:` without quotes | `with Input:email=...` | Quote it: `with \"Input:email\"=...` |\n| `...` as variable default | `my_var: mutable string = ...` | Use `\"\"` for defaults; `...` is slot-filling only |\n| Vague action description | `description: \"Does a search\"` | Be specific: `description: \"Searches KB for articles matching the query\"` |\n\n---\n\n---\n\n## 6. JSON Parsing Pattern\n\nAgent Script cannot parse JSON strings inline. When an action returns a JSON string that needs to be decomposed into individual fields, use a Flow or Apex action to parse it.\n\n### Problem\n\n```agentscript\n# This will NOT work - cannot parse JSON inline in Agent Script\nset @variables.name = @outputs.json_response[\"name\"] # Not supported\nset @variables.email = @outputs.json_response.email # May work for objects, not JSON strings\n```\n\n### Solution: Flow/Apex JSON Parser\n\n```agentscript\n# Step 1: Call the action that returns JSON\nget_data: @actions.fetch_external_data\n with endpoint = \"customer_profile\"\n set @variables.raw_json = @outputs.response_body\n\n# Step 2: Pass JSON to a parser action (Flow or Apex)\nparse: @actions.parse_json_response\n with json_string = @variables.raw_json\n set @variables.customer_name = @outputs.name\n set @variables.customer_email = @outputs.email\n set @variables.customer_tier = @outputs.tier\n```\n\n### Apex Parser Example\n\n```apex\npublic class JsonParserAction {\n public class ParseRequest {\n @InvocableVariable(required=true)\n public String jsonString;\n }\n\n public class ParseResult {\n @InvocableVariable public String name;\n @InvocableVariable public String email;\n @InvocableVariable public String tier;\n }\n\n @InvocableMethod(label='Parse Customer JSON')\n public static List\u003cParseResult> parse(List\u003cParseRequest> requests) {\n List\u003cParseResult> results = new List\u003cParseResult>();\n for (ParseRequest req : requests) {\n Map\u003cString, Object> parsed = (Map\u003cString, Object>) JSON.deserializeUntyped(req.jsonString);\n ParseResult result = new ParseResult();\n result.name = (String) parsed.get('name');\n result.email = (String) parsed.get('email');\n result.tier = (String) parsed.get('tier');\n results.add(result);\n }\n return results;\n }\n}\n```\n\n> **Tip**: For complex nested JSON, create typed Apex wrapper classes instead of using `deserializeUntyped`.\n\n---\n\n*Consolidated from @kunello's [PR #20](https://github.com/Jaganpro/sf-skills/pull/20) research on Agent Script Recipes action configuration patterns.*\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":10773,"content_sha256":"c0c410d40adad6ee2645bbbf013ffc3438fa44f4a9dc8806bf9453c3288e3a9c"},{"filename":"references/action-prompt-templates.md","content":"\u003c!-- Parent: sf-ai-agentscript/SKILL.md -->\n# Prompt Template Actions\n\n> Invoking Salesforce Prompt Templates as actions within Agent Script\n\n**Related**: [SKILL.md — Action Target Protocols](../SKILL.md) | [action-patterns.md](action-patterns.md)\n\n---\n\n## Overview\n\nPrompt Template Actions let agents invoke Salesforce Prompt Builder templates via either the `prompt://` shorthand or the equivalent `generatePromptResponse://` protocol. The agent passes structured inputs to the template and receives a generated `promptResponse` output — keeping content generation in the template while the agent manages conversation flow.\n\n> **Terminology:** in Setup, users usually say **Prompt Template**. In source metadata, the current metadata type is **`GenAiPromptTemplate`**.\n\n**When to use**: Personalized responses, summarization, content generation, recommendations — anything where an LLM prompt template produces better output than static Flow logic.\n\n---\n\n## Action Definition\n\nWhether you author the action through Setup or directly in an `.agent` file, the key prompt-template rules are the same:\n\n```agentscript\nactions:\n Generate_Personalized_Schedule:\n description: \"Generate a personalized schedule using a prompt template\"\n inputs:\n \"Input:email\": string\n description: \"User's email address\"\n is_required: True\n \"Input:preferences\": string\n description: \"User's scheduling preferences\"\n is_required: False\n outputs:\n promptResponse: string\n description: \"The personalized schedule generated by the template\"\n is_displayable: False\n is_used_by_planner: True\n target: \"prompt://Generate_Personalized_Schedule\"\n```\n\n### Critical syntax rules\n\n| Rule | Example |\n|------|---------|\n| Preferred target protocol | `\"prompt://TemplateName\"` |\n| Also valid | `\"generatePromptResponse://TemplateName\"` |\n| Input names **must be quoted** | `\"Input:email\"` not `Input:email` |\n| Input prefix is `Input:` | Matches the template's input field API name |\n| Output field is always `promptResponse` | Single string output from the template |\n\n> Both target protocols compile to the same runtime target. Prefer `prompt://` for brevity in new authoring.\n\n### Prompt Template Gotchas\n\n#### 1) Quote every `Input:...` binding\n\nParameter names that include `:` must stay quoted in both the action definition and the invocation.\n\n```agentscript\n# ✅ Correct\ninputs:\n \"Input:email\": string\n is_required: True\n\n# ❌ Wrong\ninputs:\n Input:email: string\n```\n\n#### 2) `...` belongs to LLM-invoked actions, not deterministic inline `run`\n\n```agentscript\n# ❌ Wrong — deterministic run cannot slot-fill\nreasoning:\n instructions: ->\n run @actions.Generate_Order_Summary\n with \"Input:orderId\" = ...\n```\n\n```agentscript\n# ✅ Correct — let the LLM invoke the action and fill the input\nreasoning:\n instructions: ->\n | Ask for the order number, then call {[email protected]_summary}.\n actions:\n generate_summary: @actions.Generate_Order_Summary\n description: \"Generate a summary for the requested order\"\n with \"Input:orderId\" = ...\n set @variables.summary = @outputs.promptResponse\n```\n\nIf you need a later deterministic `run`, capture the user input first with `@utils.setVariables` (or another LLM-invoked action), then reference the saved variable.\n\n#### 3) Prompt outputs usually belong to the planner, not direct display\n\nFor most prompt-template actions, the safest output pattern remains:\n\n```agentscript\noutputs:\n promptResponse: string\n is_displayable: False\n is_used_by_planner: True\n```\n\n### Recommended output flags\n\nFor prompt targets, treat `promptResponse` as planner input rather than raw UI output:\n\n```agentscript\noutputs:\n promptResponse: string\n description: \"Generated response text\"\n is_displayable: False\n is_used_by_planner: True\n```\n\nWhy:\n- `is_displayable: True` on prompt outputs is a known blank-response trap\n- if the output is hidden but not planner-visible, the action may run but the response can still feel \"missing\"\n- `filter_from_agent: True` is **not** the best substitute when the planner needs the prompt output, because `filter_from_agent` and `is_used_by_planner` cannot safely share the same field in this runtime\n\n---\n\n## Agent Script Invocation\n\nReference the action definition in your `.agent` file:\n\n```agentscript\ntopic schedule_generation:\n reasoning:\n actions:\n generate_schedule: @actions.Generate_Personalized_Schedule\n with \"Input:email\"[email protected]_email\n \"Input:preferences\"=...\n set @variables.schedule = @outputs.promptResponse\n```\n\n**Input binding patterns** (same as regular actions):\n- `@variables.user_email` — variable binding (data from prior turns)\n- `...` — LLM slot-filling (extract from conversation)\n- `\"professional\"` — fixed value (business rule constant)\n\n---\n\n## Grounded Data Integration\n\nTemplates can include **data providers** (Apex classes, Flows) that supply contextual data for personalized responses. The grounding happens inside the template — Agent Script only needs to pass the lookup key:\n\n```agentscript\nactions:\n Get_Product_Recommendations:\n description: \"Generate personalized product recommendations based on purchase history\"\n inputs:\n \"Input:customerId\": string\n description: \"Customer ID for personalization\"\n is_required: True\n outputs:\n promptResponse: string\n description: \"Personalized recommendations grounded in customer data\"\n is_displayable: False\n is_used_by_planner: True\n target: \"prompt://Product_Recommender\"\n```\n\nThe template itself (configured in Prompt Builder) includes:\n- **Data Provider**: Apex class fetching customer purchase history\n- **Grounding**: Recent orders, preferences, browsing history\n- **Template instructions**: How to format recommendations using the grounded data\n\n---\n\n## Common Patterns\n\n### Pattern 1: Content Generation\n\n```agentscript\ngenerate_email: @actions.Generate_Email_Response\n with \"Input:customerMessage\"[email protected]_message\n \"Input:tone\"=\"professional\"\n \"Input:context\"[email protected]_context\n set @variables.email_draft = @outputs.promptResponse\n```\n\n### Pattern 2: Summarization\n\n```agentscript\nsummarize: @actions.Summarize_Conversation\n with \"Input:conversationHistory\"[email protected]_history\n \"Input:maxLength\"=\"500\"\n set @variables.summary = @outputs.promptResponse\n```\n\n### Pattern 3: Personalized Recommendations\n\n```agentscript\nrecommend: @actions.Get_Product_Recommendations\n with \"Input:customerId\"[email protected]_id\n \"Input:category\"=...\n set @variables.recommendations = @outputs.promptResponse\n```\n\n---\n\n## Known Limitation: `run` Keyword with Prompt Templates\n\nChained actions using `run` may not properly map `\"Input:X\"` parameters:\n\n```agentscript\n# ❌ MAY NOT WORK — run + prompt template input binding:\nprocess: @actions.create_order\n with [email protected]_id\n run @actions.Generate_Order_Summary\n with \"Input:orderId\"[email protected]_id # Input binding may fail\n\n# ✅ WORKAROUND — call as primary action instead:\ngenerate_summary: @actions.Generate_Order_Summary\n with \"Input:orderId\"[email protected]_id # Works as primary action\n set @variables.summary = @outputs.promptResponse\n```\n\n> See SKILL.md \"Action Chaining with `run` Keyword\" for the full known bug description.\n\n---\n\n\u003ca id=\"draft-template-publish-errors\">\u003c/a>\n\n## Draft Template Publish Errors (Critical)\n\n> **If your `generatePromptResponse://` action fails to publish, check the template status FIRST.** A Draft template produces a cascade of misleading errors — none of which mention the template status.\n\nWhen the target prompt template is in **Draft** status, `sf agent publish authoring-bundle` reports different errors depending on which I/O blocks are present. All are caused by the same root cause:\n\n| Agent Script State | Error Message | Actually Means |\n|---|---|---|\n| Full I/O (`inputs:` + `outputs:`) | `invalid input parameters found: 'Input:query'` | Template can't be resolved (Draft) |\n| `outputs:` only | `invalid output parameters found: 'promptResponse'` | Template can't be resolved (Draft) |\n| Bare definition (no I/O) | `Metadata API request failed: Metadata retrieval failed:` | Template can't be resolved (Draft) |\n| Bare definition (retry) | `Internal Error, try again later` | Template can't be resolved (Draft) |\n\n**`sf agent validate authoring-bundle` passes regardless of template status** — it does not check whether the target template is published / active. This gives false confidence before publish.\n\n**Fix**: Publish or activate the template in **Setup > Prompt Builder** before publishing the agent. The original Agent Script syntax (with `\"Input:X\"` inputs and `promptResponse` output) is correct — it will publish successfully once the template is available for runtime use.\n\n**Pre-publish check**:\n```bash\nsf project retrieve start --metadata GenAiPromptTemplate:YourTemplateName -o TARGET_ORG --json\n# Then inspect the retrieved XML for \u003cstatus>Published\u003c/status> or \u003cstatus>Active\u003c/status>\n# Draft status = publish will fail with misleading errors\n```\n\n> See [known-issues.md](known-issues.md#issue-40) Issue 40 for the full bug report.\n\n---\n\n## Common Errors\n\n| Error | Cause | Fix |\n|-------|-------|-----|\n| `SyntaxError` on input binding | Missing quotes on parameter name | Use `\"Input:email\"` not `Input:email` |\n| Template not found | Wrong protocol or template name | Verify `prompt://ExactTemplateName` (or the equivalent `generatePromptResponse://ExactTemplateName`) |\n| Empty `promptResponse` | Template not published / active, or missing required inputs | Publish the template in Setup, check all `is_required: True` inputs are bound |\n| Blank agent reply even though template ran | `is_displayable: True` on `promptResponse` | Set `is_displayable: False` and let the planner synthesize the reply |\n| Planner behaves like the prompt output is missing | Output is hidden but not planner-visible | Set `is_used_by_planner: True` on the output that should drive the reply or routing |\n| Input not mapped | API name mismatch | Input field name after `Input:` must exactly match template's input API name |\n| `invalid input/output parameters found` | Target template is in **Draft** status | Publish the template in Setup > Prompt Builder — see \"Draft Template Publish Errors\" above |\n| `Internal Error` with bare action definition | Missing I/O blocks AND/OR Draft template | Add `inputs:`/`outputs:` blocks and ensure the template is published / active |\n\n---\n\n## Checklist\n\n- [ ] Template exists in org and is **published / active** (Draft templates cause misleading publish errors)\n- [ ] Input field API names match template configuration exactly\n- [ ] All `is_required: True` inputs are bound (via `...`, `@variables`, or fixed)\n- [ ] `promptResponse` output is captured with `set`\n- [ ] `promptResponse` uses `is_displayable: False` + `is_used_by_planner: True` when the planner should synthesize the reply\n- [ ] Template response quality tested with representative inputs\n- [ ] If using grounded data: data provider returns expected records\n\n---\n\n*Consolidated from repository prompt-template integration research and validated action configuration patterns.*\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":11350,"content_sha256":"fd33266cc86f5d16479881e0b87719f7ea40e8ba56b78f0eb3c48be9f4c625f2"},{"filename":"references/actions-reference.md","content":"\u003c!-- Parent: sf-ai-agentscript/SKILL.md -->\n# Actions Reference\n\n> Migrated from the former `sf-ai-agentforce-legacy/references/actions-reference.md` on 2026-02-07.\n> For context-aware descriptions, instruction references, and input binding patterns, see [action-patterns.md](action-patterns.md).\n> For prompt template actions (`generatePromptResponse://`), see [action-prompt-templates.md](action-prompt-templates.md).\n\nComplete guide to Agent Actions in Agentforce: Flow, Apex, API actions,\nescalation routing, and GenAiFunction metadata.\n\n---\n\n## Action Properties Reference\n\nAll actions in Agent Script support these properties:\n\n### Action Definition Properties\n\n| Property | Type | Required | Description | TDD |\n|----------|------|----------|-------------|-----|\n| `target` | String | Yes | Executable target (see Action Target Types below) | v1.3.0 |\n| `description` | String | Yes | Explains behavior for LLM decision-making | v1.3.0 |\n| `label` | String | No | Display name in UI (valid on action definitions, topics, and I/O fields) | v2.2.0 |\n| `inputs` | Object | No | Input parameters and requirements | v1.3.0 |\n| `outputs` | Object | No | Return parameters | v1.3.0 |\n| `available_when` | Expression | No | Conditional availability for the LLM | v1.3.0 |\n| `require_user_confirmation` | Boolean | No | Ask user to confirm before execution (compiles; runtime no-op per Issue 6) | v2.2.0 |\n| `include_in_progress_indicator` | Boolean | No | Show progress indicator during execution | v2.2.0 |\n| `progress_indicator_message` | String | No | Custom message shown during execution (e.g., \"Processing your request...\") | v2.2.0 |\n\n> **Note**: `label`, `require_user_confirmation`, `include_in_progress_indicator`, and `progress_indicator_message` are valid on action definitions with `target:` but NOT on `@utils.transition` utility actions (see Val_Action_Properties vs Val_Action_Meta_Props).\n\n### Input Properties\n\n| Property | Type | Description | TDD |\n|----------|------|-------------|-----|\n| `description` | String | Explains the input parameter to LLM | v1.3.0 |\n| `label` | String | Display name in UI | v2.2.0 |\n| `is_required` | Boolean | Marks input as mandatory for the LLM | v2.2.0 |\n| `is_user_input` | Boolean | LLM extracts value from conversation context | v2.2.0 |\n| `complex_data_type_name` | String | Lightning data type mapping | v2.1.0 |\n\n### Output Properties\n\n| Property | Type | Description | TDD |\n|----------|------|-------------|-----|\n| `description` | String | Explains the output parameter | v1.3.0 |\n| `label` | String | Display name in UI | v2.2.0 |\n| `filter_from_agent` | Boolean | `True` = hide from user display (GA standard name) | v1.3.0 |\n| `is_displayable` | Boolean | `False` = hide from user (compile-valid alias for `filter_from_agent`). Prefer this on prompt outputs when the planner still needs the value | v2.2.0 |\n| `is_used_by_planner` | Boolean | `True` = LLM can reason about this value for routing decisions | v2.2.0 |\n| `developer_name` | String | Overrides the parameter's developer name | — |\n| `complex_data_type_name` | String | Lightning data type mapping | v2.1.0 |\n\n> **`filter_from_agent` vs `is_displayable`**: Both control the same behavior. `filter_from_agent: True` is the GA standard name (used in official documentation). `is_displayable: False` is a compile-valid alias that achieves the same result.\n\n> ⚠️ **Prompt-template exception**: On `prompt://` / `generatePromptResponse://` targets, prefer `is_displayable: False` + `is_used_by_planner: True` when the planner should synthesize the reply. `is_displayable: True` is a blank-response trap for prompt outputs.\n\n> ⚠️ **`filter_from_agent` and `is_used_by_planner` are mutually exclusive**: Do NOT use both on the same output field. The combination causes `InvalidFormatError` which invalidates the entire action definition, triggering cascading `ACTION_NOT_IN_SCOPE` errors on every reference to that action. Use `filter_from_agent: True` alone to hide outputs from the customer. See [known-issues.md](known-issues.md#issue-40-filter-planner-conflict) Issue 40.\n\n### Output Access Follows the Output Schema\n\nDo not assume `@outputs.X` is always a scalar.\n\n```agentscript\n# Scalar output\nset @variables.status = @outputs.status\n\n# Structured/wrapper output\nset @variables.raw_result = @outputs.output\nset @variables.result_text = @outputs.output.value # only if the schema exposes 'value'\n```\n\n**Guidance:**\n- Direct assignment is safe for scalar outputs (`string`, `number`, `boolean`).\n- If an action returns `object` or another structured type, inspect the output schema before comparing or assigning it.\n- `.value` is a common wrapper field, but not a universal rule.\n- When a deterministic branch depends on the result, flatten the Flow/Apex output so the agent receives an explicit scalar.\n\n### Example with All Properties\n\n```agentscript\nactions:\n process_payment:\n description: \"Processes payment for the order\"\n require_user_confirmation: True # Ask user before executing\n include_in_progress_indicator: True\n inputs:\n amount: number\n description: \"Payment amount\"\n card_token: string\n description: \"Tokenized card number\"\n outputs:\n transaction_id: string\n description: \"Transaction reference\"\n card_last_four: string\n description: \"Last 4 digits of card\"\n filter_from_agent: True # Hide from direct customer display\n target: \"flow://Process_Payment\"\n available_when: @variables.cart_total > 0\n```\n\n---\n\n## Action Target Types (Complete Reference)\n\nAgentScript supports **22+ action target types**. Use the correct protocol for your integration:\n\n| Short Name | Long Name | Description | Use Case |\n|------------|-----------|-------------|----------|\n| `flow` | `flow` | Salesforce Flow | Most common — Autolaunched Flows |\n| `apex` | `apex` | Apex Class | Custom business logic |\n| `prompt` | `generatePromptResponse` | Prompt Template | AI-generated responses |\n| `standardInvocableAction` | `standardInvocableAction` | Built-in Salesforce actions | Send email, create task, etc. |\n| `externalService` | `externalService` | External API via OpenAPI schema | External system calls |\n| `quickAction` | `quickAction` | Object-specific quick actions | Log call, create related record |\n| `api` | `api` | REST API calls | Direct API invocation |\n| `apexRest` | `apexRest` | Custom REST endpoints | Custom @RestResource classes |\n| `serviceCatalog` | `createCatalogItemRequest` | Service Catalog | Service catalog requests |\n| `integrationProcedureAction` | `executeIntegrationProcedure` | OmniStudio Integration | Industry Cloud procedures |\n| `expressionSet` | `runExpressionSet` | Expression calculations | Decision matrix, calculations |\n| `cdpMlPrediction` | `cdpMlPrediction` | CDP ML predictions | Data Cloud predictions |\n| `externalConnector` | `externalConnector` | External system connector | Pre-built connectors |\n| `slack` | `slack` | Slack integration | Slack messaging |\n| `namedQuery` | `namedQuery` | Predefined queries | Saved SOQL queries |\n| `auraEnabled` | `auraEnabled` | Lightning component methods | @AuraEnabled Apex methods |\n| `mcpTool` | `mcpTool` | Model Context Protocol | MCP tool integrations |\n| `retriever` | `retriever` | Knowledge retrieval | RAG/knowledge base queries |\n\n**Target Format**: `\u003ctype>://\u003cDeveloperName>` (e.g., `flow://Get_Account_Info`, `standardInvocableAction://sendEmail`)\n\n> 💡 `prompt://` is the official shorthand for `generatePromptResponse://`. Both forms resolve to the same target.\n\n**Common Examples:**\n```agentscript\n# Flow action (most common)\ntarget: \"flow://Get_Customer_Orders\"\n\n# Apex action\ntarget: \"apex://CustomerServiceController\"\n\n# Prompt template\ntarget: \"generatePromptResponse://Email_Draft_Template\"\n\n# Standard invocable action (built-in Salesforce)\ntarget: \"standardInvocableAction://sendEmail\"\n\n# External service (API call)\ntarget: \"externalService://Stripe_Payment_API\"\n```\n\n**Tip**: Before creating a custom Flow, check if a `standardInvocableAction://` already exists for your use case.\n\n### Pre-Publish Target Validation\n\nBefore `sf agent publish`, verify action targets exist and are active:\n\n| Target Protocol | Validation Query |\n|-----------------|-----------------|\n| `flow://X` | `SELECT ApiName FROM FlowDefinitionView WHERE ApiName = 'X' AND IsActive = true` |\n| `apex://X` | `SELECT Name FROM ApexClass WHERE Name = 'X'` |\n| `retriever://X` | `SELECT DeveloperName FROM DataCloudRetriever WHERE DeveloperName = 'X'` |\n| `externalService://X` | `SELECT DeveloperName FROM ExternalServiceRegistration WHERE DeveloperName = 'X'` |\n| `generatePromptResponse://X` | `SELECT DeveloperName FROM PromptTemplate WHERE DeveloperName = 'X' AND Status IN ('Active', 'Published')` |\n\nRun these via: `sf data query -q \"QUERY\" -o ORG --json`\n\n> **Prompt template note**: the queryable object name may still appear as `PromptTemplate`, while the current source metadata type is `GenAiPromptTemplate`.\n\n### Direct Action Invocation (Debugging)\n\nTest action targets outside the agent context via REST API:\n- Flow: `POST /services/data/v66.0/actions/custom/flow/{FlowApiName}`\n- Apex: `POST /services/data/v66.0/actions/custom/apex/{ClassName}`\n\nUseful for isolating whether failures are in the action target or the agent framework.\n\n---\n\n## Action Invocation Methods\n\n| Method | Syntax | Behavior | AiAuthoringBundle | GenAiPlannerBundle |\n|--------|--------|----------|-------------------|-------------------|\n| **Actions Block** | `actions:` in `reasoning:` | LLM chooses which to execute | ✅ Works | ✅ Works |\n| **Deterministic** | `run @actions.name` | Always executes when code path is reached **if `name` resolves to a topic-level target-backed action definition** | ⚠️ Partial (see below) | ✅ Works |\n\n### Deployment Method Capabilities\n\n**`run` keyword IS supported in `reasoning.actions:` post-action blocks and `instructions: ->` blocks (Validated Jan 2026)**\n\n```agentscript\n# ✅ WORKS — run in reasoning.actions post-action block\ncreate: @actions.create_order\n with customer_id = @variables.customer_id\n run @actions.send_confirmation # ✅ Chains after create_order\n set @variables.order_id = @outputs.id\n\n# ✅ WORKS — run in instructions: -> block\nreasoning:\n instructions: ->\n run @actions.load_customer\n with id = @variables.customer_id\n set @variables.name = @outputs.name\n\n# ❌ DOES NOT WORK — run in before_reasoning (no LLM context)\nbefore_reasoning:\n run @actions.log_turn # ❌ May not execute as expected\n```\n\n> **Note**: The Dec 2025 finding that `run` was \"NOT supported\" has been superseded. As of Jan 2026, `run` works in post-action chains and procedural instruction blocks **when it targets a topic-level action definition with `target:`**. It does NOT work reliably in `before_reasoning:`. Do **not** use `run` for utilities / delegations (`@utils.setVariables`, `@utils.transition`, `@topic.X`) even if they are written under an `actions:` block — those are not deterministic `run` targets.\n\n**`{[email protected]}` interpolation in instructions (Updated Feb 2026)**\n\nThe `{[email protected]_name}` syntax embeds a reference to the full action definition into reasoning instructions. This gives the LLM richer context about available actions.\n\n```agentscript\n# ✅ WORKS — reference action in instructions for guided selection\nreasoning:\n instructions: ->\n | To look up an order, use {[email protected]_order}.\n | To check shipping status, use {[email protected]_shipment}.\n```\n\n> **Note**: The Dec 2025 finding that this was broken has been superseded. See [action-patterns.md](action-patterns.md#2-instruction-action-references) for detailed usage patterns and examples.\n\n### Correct Approach: Use `reasoning.actions` Block\n\nThe LLM automatically selects appropriate actions from those defined in the `reasoning.actions` block:\n\n```agentscript\ntopic order_management:\n description: \"Handles order inquiries\"\n\n actions:\n get_order:\n description: \"Retrieves order information\"\n inputs:\n order_id: string\n description: \"The order ID\"\n outputs:\n status: string\n description: \"Order status\"\n target: \"flow://Get_Order_Details\"\n\n reasoning:\n instructions: ->\n | Help the customer with their order.\n | When they ask about an order, look it up.\n actions:\n # LLM automatically selects this when appropriate\n lookup: @actions.get_order\n with order_id=...\n set @variables.order_status = @outputs.status\n```\n\n---\n\n## Action Type 1: Flow Actions\n\n### When to Use\n\n- Standard Salesforce data operations (CRUD)\n- Business logic that can be expressed in Flow\n- Screen flows for guided user experiences\n- Approval processes\n\n### Implementation\n\n```yaml\nactions:\n create_case:\n description: \"Creates a new support case for the customer\"\n inputs:\n subject:\n type: string\n description: \"Case subject line\"\n description:\n type: string\n description: \"Detailed case description\"\n priority:\n type: string\n description: \"Case priority (Low, Medium, High, Urgent)\"\n outputs:\n caseNumber:\n type: string\n description: \"Created case number\"\n caseId:\n type: string\n description: \"Case record ID\"\n target: \"flow://Create_Support_Case\"\n```\n\n### Flow Requirements\n\nFor an action to work with agents, the Flow must:\n\n1. **Be Autolaunched** — `processType: AutoLaunchedFlow`\n2. **Have Input Variables** — Marked as `isInput: true`\n3. **Have Output Variables** — Marked as `isOutput: true`\n4. **Be Active** — `status: Active`\n\n**Flow Variable Example:**\n```xml\n\u003cvariables>\n \u003cname>subject\u003c/name>\n \u003cdataType>String\u003c/dataType>\n \u003cisCollection>false\u003c/isCollection>\n \u003cisInput>true\u003c/isInput>\n \u003cisOutput>false\u003c/isOutput>\n\u003c/variables>\n```\n\n### Best Practices\n\n| Practice | Description |\n|----------|-------------|\n| Descriptive names | Use clear Flow API names that describe the action |\n| Error handling | Include fault paths in your Flow |\n| Bulkification | Design Flows to handle multiple records |\n| Governor limits | Avoid SOQL/DML in loops |\n\n---\n\n## Action Type 2: Apex Actions\n\n### When to Use\n\n- Complex calculations or algorithms\n- Custom integrations requiring Apex\n- Operations not possible in Flow\n- Bulk data processing\n- When you need full control over execution\n\n### Two Deployment Paths (CRITICAL DISTINCTION)\n\n| Deployment Method | How Apex Actions Work | GenAiFunction Required? |\n|-------------------|----------------------|------------------------|\n| **AiAuthoringBundle** (.agent file) | `apex://ClassName` target in topic actions block | **NO** |\n| **Agent Builder UI** (GenAiPlannerBundle) | GenAiFunction metadata wraps the Apex class | **YES** |\n\n> ⚠️ **The official [agent-script-recipes](https://github.com/trailheadapps/agent-script-recipes) repo uses `apex://ClassName` directly with ZERO GenAiFunction metadata.** GenAiFunction is only needed when configuring agents through the Agent Builder UI or deploying via GenAiPlannerBundle.\n\n### Path A: AiAuthoringBundle (Agent Script — RECOMMENDED)\n\n#### Step 1: Create Apex Class with @InvocableMethod\n\n```apex\npublic with sharing class CalculateDiscountAction {\n\n public class DiscountRequest {\n @InvocableVariable(label='Order Amount' required=true)\n public Decimal orderAmount;\n\n @InvocableVariable(label='Customer Tier' required=true)\n public String customerTier;\n }\n\n public class DiscountResult {\n @InvocableVariable(label='Discount Percentage')\n public Decimal discountPercentage;\n\n @InvocableVariable(label='Final Amount')\n public Decimal finalAmount;\n }\n\n @InvocableMethod(\n label='Calculate Discount'\n description='Calculates discount based on order amount and customer tier'\n )\n public static List\u003cDiscountResult> calculateDiscount(List\u003cDiscountRequest> requests) {\n List\u003cDiscountResult> results = new List\u003cDiscountResult>();\n for (DiscountRequest req : requests) {\n DiscountResult result = new DiscountResult();\n result.discountPercentage = getTierDiscount(req.customerTier);\n result.finalAmount = req.orderAmount * (1 - result.discountPercentage / 100);\n results.add(result);\n }\n return results;\n }\n\n private static Decimal getTierDiscount(String tier) {\n Map\u003cString, Decimal> tierDiscounts = new Map\u003cString, Decimal>{\n 'Bronze' => 5, 'Silver' => 10, 'Gold' => 15, 'Platinum' => 20\n };\n return tierDiscounts.containsKey(tier) ? tierDiscounts.get(tier) : 0;\n }\n}\n```\n\n#### Step 2: Reference DIRECTLY in Agent Script via `apex://`\n\n```yaml\ntopic discount_calculator:\n description: \"Calculates discount for customer order\"\n\n # Level 1: Action DEFINITION with target\n actions:\n calculate_discount:\n description: \"Calculates discount based on order amount and customer tier\"\n inputs:\n orderAmount: number\n description: \"The total order amount before discount\"\n customerTier: string\n description: \"Customer membership tier\"\n outputs:\n discountPercentage: number\n description: \"Applied discount percentage\"\n finalAmount: number\n description: \"Final order amount after discount\"\n target: \"apex://CalculateDiscountAction\" # Direct Apex — NO GenAiFunction needed!\n\n reasoning:\n instructions: |\n Help the customer calculate their discount.\n # Level 2: Action INVOCATION referencing the Level 1 definition\n actions:\n calc: @actions.calculate_discount\n with orderAmount=...\n with [email protected]\n set @variables.final_amount = @outputs.finalAmount\n```\n\n> ✅ **No GenAiFunction, no GenAiPlugin, no metadata deployment beyond the Apex class itself.** The `apex://ClassName` target auto-discovers the `@InvocableMethod` on the class.\n\n#### I/O Name Matching Rules (TDD Validated v2.1.0)\n\nAction `inputs:` and `outputs:` names in Agent Script must **exactly match** the `@InvocableVariable` field names in the Apex class:\n\n| Agent Script I/O Name | Apex @InvocableVariable Field | Result |\n|------------------------|-------------------------------|--------|\n| `orderAmount` | `public Decimal orderAmount` | ✅ Publishes |\n| `order_amount` | `public Decimal orderAmount` | ❌ `invalid input 'order_amount'` |\n| `wrong_name` | `public String outputText` | ❌ `invalid output 'wrong_name'` |\n| *(subset of outputs)* | *(declares fewer than all)* | ✅ Publishes (partial is valid) |\n\n> **Partial Output Pattern**: You can declare a **subset** of the target's outputs in your action definition — you don't need to map every output parameter. This is useful when you only need one field from a multi-output action.\n\n#### Bare @InvocableMethod Pattern (NOT Compatible)\n\n> ⚠️ **TDD Finding (v2.1.0)**: Apex classes using bare `List\u003cString>` parameters (no `@InvocableVariable` wrapper classes) are **incompatible** with Agent Script actions. The framework cannot discover bindable parameter names without `@InvocableVariable` annotations.\n\n```apex\n// ❌ INCOMPATIBLE with Agent Script — bare parameters, no wrappers\npublic class BareAction {\n @InvocableMethod(label='Bare Action')\n public static List\u003cString> execute(List\u003cString> inputs) {\n return inputs;\n }\n}\n\n// ✅ COMPATIBLE — wrapper classes with @InvocableVariable\npublic class WrappedAction {\n public class Request {\n @InvocableVariable(label='Input Text' required=true)\n public String inputText; // ← This field name becomes the I/O name\n }\n public class Response {\n @InvocableVariable(label='Output Text')\n public String outputText; // ← This field name becomes the I/O name\n }\n @InvocableMethod(label='Wrapped Action')\n public static List\u003cResponse> execute(List\u003cRequest> requests) { ... }\n}\n```\n\n**Rule**: Always use `@InvocableVariable` wrapper classes when your Apex action will be called from Agent Script.\n\n> ⚠️ **Namespace Warning (Unresolved)**: In namespaced packages, `apex://ClassName` may fail at publish time with \"invocable action does not exist,\" even when the Apex class is confirmed deployed via SOQL. It is unclear whether namespace prefix syntax is required (e.g., `apex://ns__ClassName`). If you encounter this in a namespaced org, try: (1) `apex://ns__ClassName` format, (2) wrapping the Apex in a Flow and using `flow://` instead. See [known-issues.md](known-issues.md#issue-2-sf-agent-publish-fails-with-namespace-prefix-on-apex-targets) for tracking.\n\n### Path B: Agent Builder UI (GenAiPlannerBundle — Legacy)\n\nIf you're NOT using Agent Script and are building agents through the Agent Builder UI, you need GenAiFunction metadata to wrap the Apex class. See the `assets/metadata/genai-function-apex.xml` template for the XML format.\n\n> **Note**: GenAiFunction XML (API v66.0) only supports these elements: `masterLabel`, `description`, `invocationTarget`, `invocationTargetType`, `isConfirmationRequired`. Input/output schemas are defined via `input/schema.json` and `output/schema.json` bundle files, NOT inline XML elements.\n\n---\n\n## Action Type 3: API Actions (External System Integration)\n\n### Architecture\n\n```\n┌─────────────────────────────────────────────────────────────┐\n│ API ACTION ARCHITECTURE │\n├─────────────────────────────────────────────────────────────┤\n│ Agent Script │\n│ │ │\n│ ▼ │\n│ flow://HTTP_Callout_Flow │\n│ │ │\n│ ▼ │\n│ HTTP Callout Action (in Flow) │\n│ │ │\n│ ▼ │\n│ Named Credential (Authentication) │\n│ │ │\n│ ▼ │\n│ External API │\n└─────────────────────────────────────────────────────────────┘\n```\n\n### Implementation Steps\n\n1. **Create Named Credential** (via sf-integration skill)\n2. **Create HTTP Callout Flow** wrapping the external call\n3. **Reference Flow in Agent Script** with `flow://` target\n\n### Security Considerations\n\n| Consideration | Implementation |\n|---------------|----------------|\n| Authentication | Always use Named Credentials (never hardcode secrets) |\n| Permissions | Use Permission Sets to grant Named Principal access |\n| Error handling | Implement fault paths in Flow |\n| Logging | Log callout details for debugging |\n| Timeouts | Set appropriate timeout values |\n\n---\n\n## Connection Block (Escalation Routing)\n\nThe `connection` block enables escalation to human agents via Omni-Channel. Use `connection messaging:` (singular). The plural `connections:` wrapper is invalid — see [known-issues.md](known-issues.md#issue-16).\n\n### Basic Syntax\n\n```agentscript\n# Messaging channel (most common)\nconnection messaging:\n outbound_route_type: \"OmniChannelFlow\"\n outbound_route_name: \"Support_Queue_Flow\"\n escalation_message: \"Transferring you to a human agent...\"\n adaptive_response_allowed: True\n```\n\n### Multiple Channels\n\n> ⚠️ The plural `connections:` wrapper block is **invalid** (see known-issues.md Issue 16). Use separate `connection \u003cchannel>:` blocks:\n\n```agentscript\n# Each channel gets its own top-level connection block\nconnection messaging:\n escalation_message: \"Transferring to messaging agent...\"\n outbound_route_type: \"OmniChannelFlow\"\n outbound_route_name: \"flow://Agent_Support_Flow\"\n adaptive_response_allowed: True\n```\n\n### Connection Block Properties\n\n| Property | Type | Required | Description |\n|----------|------|----------|-------------|\n| `outbound_route_type` | String | Yes | **`\"OmniChannelFlow\"` is the only TDD-validated value.** SKILL.md mentions `\"Queue\"` and `\"Skill\"` but the `connections:` block itself is not GA (see known-issues.md Issue 16). |\n| `outbound_route_name` | String | Yes | API name of Omni-Channel Flow (must exist in org) |\n| `escalation_message` | String | Yes | Message shown to user during transfer |\n| `adaptive_response_allowed` | Boolean | No | Allow agent to adapt responses during escalation (default: False) |\n\n### Supported Channels\n\n| Channel | Description | Use Case |\n|---------|-------------|----------|\n| `messaging` | Chat/messaging channels | Enhanced Chat, Web Chat, In-App |\n| `telephony` | Voice/phone channels | Service Cloud Voice, phone support |\n\n**CRITICAL**: Values like `\"queue\"`, `\"skill\"`, `\"agent\"` for `outbound_route_type` cause validation errors!\n\n### Escalation Action\n\n```agentscript\n# AiAuthoringBundle - basic escalation\nactions:\n transfer_to_human: @utils.escalate\n description: \"Transfer to human agent\"\n\n# GenAiPlannerBundle - with reason parameter\nactions:\n transfer_to_human: @utils.escalate with reason=\"Customer requested\"\n```\n\n### Prerequisites for Escalation\n\n1. Omni-Channel configured in Salesforce\n2. Omni-Channel Flow created and deployed\n3. Connection block in agent script\n4. Messaging channel active (Enhanced Chat, etc.)\n\n---\n\n## GenAiFunction Metadata Summary (Agent Builder UI / GenAiPlannerBundle ONLY)\n\n> ⚠️ **NOT needed for AiAuthoringBundle (Agent Script)**. If you're writing `.agent` files, use `target: \"apex://ClassName\"` or `target: \"flow://FlowName\"` directly. See Action Type 2 above.\n\n`GenAiFunction` wraps Apex, Flows, or Prompts as Agent Actions **for the Agent Builder UI path**.\n\n```xml\n\u003c!-- Minimal valid GenAiFunction XML (API v66.0) -->\n\u003cGenAiFunction xmlns=\"http://soap.sforce.com/2006/04/metadata\">\n \u003cdescription>What this action does\u003c/description>\n \u003cinvocationTarget>FlowOrApexName\u003c/invocationTarget>\n \u003cinvocationTargetType>flow|apex|prompt\u003c/invocationTargetType>\n \u003cisConfirmationRequired>false\u003c/isConfirmationRequired>\n \u003cmasterLabel>Display Name\u003c/masterLabel>\n\u003c/GenAiFunction>\n```\n\n**Input/Output Schemas**: Use `input/schema.json` and `output/schema.json` files in the GenAiFunction bundle directory. Do NOT use inline XML elements like `\u003cgenAiFunctionInputs>`, `\u003cgenAiFunctionOutputs>`, `\u003cgenAiFunctionParameters>`, or `\u003ccapability>` — these are NOT valid in the Metadata API XML schema (API v66.0).\n\n### Prompt Builder Template Types\n\n| Current metadata value | Typical use case |\n|------|----------|\n| `einstein_gpt__flex` | General purpose, maximum flexibility |\n| `einstein_gpt__salesEmail` | Sales-oriented generated content |\n| `einstein_gpt__fieldCompletion` | Suggest field values |\n| `einstein_gpt__recordSummary` | Summarize record data |\n\n### Template Variable Types\n\n| Variable Type | Description |\n|---------------|-------------|\n| `freeText` | User-provided text input |\n| `recordField` | Bound to specific record field |\n| `relatedList` | Data from related records |\n| `resource` | Static resource content |\n\n---\n\n## Cross-Skill Integration\n\n### Orchestration Order for API Actions\n\nWhen building agents with external API integrations, follow this order:\n\n```\n┌─────────────────────────────────────────────────────────────┐\n│ INTEGRATION + AGENTFORCE ORCHESTRATION ORDER │\n├─────────────────────────────────────────────────────────────┤\n│ 1. sf-connected-apps → Connected App (if OAuth needed) │\n│ 2. sf-integration → Named Credential + External Service │\n│ 3. sf-apex → @InvocableMethod (if custom logic) │\n│ 4. sf-flow → Flow wrapper (HTTP Callout / Apex) │\n│ 5. sf-deploy → Deploy all metadata to org │\n│ 6. sf-ai-agentscript → Agent with flow:// target │\n│ 7. sf-deploy → Publish (sf agent publish │\n│ authoring-bundle) │\n└─────────────────────────────────────────────────────────────┘\n```\n\n---\n\n## Troubleshooting\n\n| Issue | Cause | Solution |\n|-------|-------|----------|\n| `Tool target 'X' is not an action definition` | Action not defined in topic `actions:` block, or target doesn't exist in org | Define action with `target:` in topic-level `actions:` block; ensure Apex class/Flow is deployed |\n| `invalid input 'X'` or `invalid output 'X'` | I/O name doesn't match `@InvocableVariable` field name in Apex | Use exact field names from the Apex wrapper class (case-sensitive) |\n| `Internal Error` with inputs-only action | Action has `inputs:` but no `outputs:` block | Add `outputs:` block — the server-side compiler requires it (see known-issues.md Issue 15) |\n| `Internal Error` with bare @InvocableMethod | Apex uses `List\u003cString>` without `@InvocableVariable` wrappers | Refactor Apex to use wrapper classes with `@InvocableVariable` annotations |\n| `apex://` target not found | Apex class not deployed or missing `@InvocableMethod` | Deploy class first, ensure it has `@InvocableMethod` annotation |\n| Flow action fails | Flow not active or not Autolaunched | Activate the Flow; ensure it's Autolaunched (not Screen) |\n| API action timeout | External system slow | Increase timeout, add retry logic |\n| Permission denied | Missing Named Principal access | Grant Permission Set |\n| Action not appearing in Agent Builder UI | GenAiFunction not deployed (UI path only) | Deploy GenAiFunction metadata (only needed for Agent Builder UI, not Agent Script) |\n\n### Debugging Tips\n\n1. **Check deployment status:** `sf project deploy report`\n2. **Verify GenAiFunction deployment:** `sf org list metadata -m GenAiFunction`\n3. **Test Flow independently:** Use Flow debugger in Setup with sample inputs\n4. **Check agent logs:** Agent Builder → Logs\n\n---\n\n## Related Documentation\n\n- [action-patterns.md](action-patterns.md) — Context-aware descriptions, instruction references, binding strategies\n- [action-prompt-templates.md](action-prompt-templates.md) — Prompt template invocation (`generatePromptResponse://`)\n- [fsm-architecture.md](fsm-architecture.md) — FSM design and node patterns\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":31458,"content_sha256":"8f5d75cccba6e4f8836b63cb4090e68e9e47d2b69155f9b52fa55150cd57f764"},{"filename":"references/activation-checklist.md","content":"# Agent Script Activation Checklist\n\nUse this file when `sf-ai-agentscript` is already active and you need the shortest possible pre-authoring / pre-publish checklist.\n\n## 1. Route correctly\n\nUse `sf-ai-agentscript` for:\n- `.agent` files\n- deterministic topic/state/action design\n- Agent Script CLI workflows (`sf agent generate/publish`)\n\nDo **not** use it for:\n- Setup UI / Agent Builder maintenance → use `sf-ai-agentforce`\n- agent testing strategy → use `sf-ai-agentforce-testing`\n- persona design → use `sf-ai-agentforce-persona`\n\n## 2. Blockers to check before editing\n\n- Exactly one `start_agent`\n- No mixed tabs and spaces\n- `True` / `False` only\n- No `else if`\n- No nested `if`\n- No top-level `actions:` block\n- No `@inputs` inside `set`\n- No default values on `linked` variables\n- No `object` / `list` linked variables\n- Use explicit `agent_type`\n- Use `run @actions.X` only for topic-level action definitions that declare `target:`\n- Do not branch directly on raw `@system_variables.user_input contains/startswith/endswith`\n- Do not treat `contains` / `startswith` / `endswith` as the sole gate for critical routing or validation\n- On prompt template outputs, prefer `is_displayable: False` + `is_used_by_planner: True`\n- Do not assume `@outputs.X` is scalar; inspect the output schema before comparing or assigning it\n\n## 3. Service Agent publish safety\n\nFor `AgentforceServiceAgent`:\n- `default_agent_user` must exist\n- user must be active\n- user must not be `AutomatedProcess`\n- `Profile.Name` must be `Einstein Agent User`\n- target-backed actions usually require a custom `{AgentName}_Access` permission set\n\nFor `AgentforceEmployeeAgent`:\n- omit `default_agent_user`\n\nFull details:\n- [agent-user-setup.md](agent-user-setup.md)\n- [production-gotchas.md](production-gotchas.md)\n\n## 4. Minimal authoring workflow\n\n1. Define `config`, `system`, `start_agent`, and topics\n2. Add `variables` only when state is needed\n3. Define target-backed actions with complete `inputs:` and `outputs:`\n4. Add deterministic guards with `available when`\n5. Validate on save\n6. Run preview smoke tests\n7. Publish, then activate\n\n## 5. Canonical deep references\n\n- Syntax rules: [syntax-reference.md](syntax-reference.md)\n- Actions and target protocols: [actions-reference.md](actions-reference.md)\n- Production and runtime gotchas: [production-gotchas.md](production-gotchas.md)\n- Preview loop: [preview-test-loop.md](preview-test-loop.md)\n- Debugging: [debugging-guide.md](debugging-guide.md)\n- Validator rules: [validator-rule-catalog.md](validator-rule-catalog.md)\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":2568,"content_sha256":"2ea427fbab35c74ba1c32399a8cb51c0395ae96a2104d0e9d6302e599489fb54"},{"filename":"references/agent-user-setup.md","content":"\u003c!-- Parent: sf-ai-agentscript/SKILL.md -->\n# Agent User Setup & Permission Model\n\n> Complete provisioning workflow for Einstein Agent Users and permission sets. Validated against representative Service Agent and Employee Agent scenarios.\n>\n> Preferred GA path: provision the Service Agent running user with `sf org create agent-user`. Use manual user creation only when you need custom user fields or the command is unavailable in the target org/tooling version.\n\n**License Requirement:** PID_DigitalAgent (typically included with Agentforce licenses)\n\n---\n\n## Agent Type Decision Matrix\n\n| Aspect | AgentforceServiceAgent | AgentforceEmployeeAgent |\n|--------|------------------------|-------------------------|\n| **Use Case** | Customer-facing, external users | Internal employees |\n| **Runs As** | Dedicated Einstein Agent User | Logged-in user |\n| **Einstein Agent User?** | Required | Not needed |\n| **System PS (`AgentforceServiceAgentBase`, `AgentforceServiceAgentUser`, `EinsteinGPTPromptTemplateUser`)** | Required | Not needed |\n| **Custom PS (`{AgentName}_Access`)** | Assigned to agent user | Assigned to employees |\n| **`default_agent_user` in config** | Required | Omit entirely |\n| **Respects Sharing Rules** | No (consistent permissions) | Yes (user's data access) |\n| **Preferred setup path** | `sf org create agent-user` | Permission-set visibility via `\u003cagentAccesses>` |\n\n> **How to check agent type**: Look at the `agent_type` field in the `config:` block of your `.agent` file, or query: `sf data query --query \"SELECT DeveloperName, Type FROM BotDefinition WHERE DeveloperName = 'AgentName'\" -o TARGET_ORG --json`\n\n---\n\n## CLI Fast Track: Complete Workflow\n\n**Preferred CLI-first workflow** (tested: ~8 minutes total):\n\n```bash\n# Step 1: Create the native Service Agent user\nsf org create agent-user --target-org TARGET_ORG --json\n\n# Optional: custom display name\nsf org create agent-user \\\n --first-name Service \\\n --last-name Agent \\\n --target-org TARGET_ORG --json\n\n# Optional: custom username base\nsf org create agent-user \\\n --base-username [email protected] \\\n --target-org TARGET_ORG --json\n\n# Step 2: Verify the native system assignments\nsf data query \\\n --query \"SELECT PermissionSet.Name FROM PermissionSetAssignment WHERE Assignee.Username = '\u003cagent-username>' ORDER BY PermissionSet.Name\" \\\n -o TARGET_ORG --json\n\n# Expected: AgentforceServiceAgentBase + AgentforceServiceAgentUser + EinsteinGPTPromptTemplateUser\n\n# Step 3: Deploy custom Permission Set for agent-specific Apex / data access\nsf project deploy start \\\n --metadata PermissionSet:\u003cAgentName>_Access \\\n -o TARGET_ORG --json\n\n# Step 4: Assign custom Permission Set\nsf org assign permset \\\n --name \u003cAgentName>_Access \\\n --on-behalf-of \u003cagent-username> \\\n -o TARGET_ORG --json\n\n# If assignment fails, inspect the full JSON payload.\n# Current CLI versions surface multiple assignment errors in --json output,\n# which is much more useful than retrying blindly.\n\n# Step 5: Verify all Permission Sets for the running user\nsf data query \\\n --query \"SELECT PermissionSet.Name, PermissionSet.Label FROM PermissionSetAssignment WHERE Assignee.Username = '\u003cagent-username>' ORDER BY PermissionSet.Name\" \\\n -o TARGET_ORG --json\n\n# Expected: AgentforceServiceAgentBase + AgentforceServiceAgentUser + EinsteinGPTPromptTemplateUser + \u003cAgentName>_Access\n\n# Step 6: Set default_agent_user in the .agent config to the returned username\n\n# Step 7: Validate and smoke-test before publish\nsf agent validate authoring-bundle --api-name \u003cAgentName> -o TARGET_ORG --json\nSESSION_ID=$(sf agent preview start --authoring-bundle \u003cAgentName> --simulate-actions -o TARGET_ORG --json | jq -r '.result.sessionId')\nsf agent preview send --session-id \"$SESSION_ID\" --authoring-bundle \u003cAgentName> --utterance \"hello\" -o TARGET_ORG --json\nsf agent preview end --session-id \"$SESSION_ID\" --authoring-bundle \u003cAgentName> -o TARGET_ORG --json\n\n# Step 8: Publish and activate\nsf agent publish authoring-bundle --api-name \u003cAgentName> -o TARGET_ORG --json\nsf agent activate --api-name \u003cAgentName> --version \u003cn> -o TARGET_ORG --json\n```\n\n**Critical Notes:**\n- `sf org create agent-user` is the preferred GA path for Service Agent running-user setup.\n- The native command auto-assigns the standard system profile and permission sets.\n- Always test with preview BEFORE publishing to avoid version management overhead.\n- Publishing does NOT activate — you must run `sf agent activate` separately.\n\n---\n\n## Manual fallback (only if native agent-user creation can't be used)\n\n### Step 1: Create Einstein Agent User\n\nService agents need a dedicated service account with consistent permissions.\n\n**Get Org ID first** (needed for username format):\n```bash\nsf org display -o TARGET_ORG --json | jq -r '.result.id'\n```\n\n**Query existing Einstein Agent Users** (skip creation if one exists):\n```bash\nsf data query --query \"SELECT Id, Username, IsActive FROM User WHERE Profile.Name = 'Einstein Agent User' AND IsActive = true\" -o TARGET_ORG --json\n```\n\n**Create the user** (if none exists):\n\n1. Get the Einstein Agent User profile ID:\n ```bash\n sf data query --query \"SELECT Id FROM Profile WHERE Name = 'Einstein Agent User'\" -o TARGET_ORG --json\n ```\n\n2. Create a user definition file (`config/einstein-agent-user.json`):\n ```json\n {\n \"Username\": \"{agent_name}_agent@{orgId}.ext\",\n \"LastName\": \"{AgentName} Agent\",\n \"Email\": \"[email protected]\",\n \"Alias\": \"agntuser\",\n \"ProfileId\": \"\u003cprofile-id-from-step-1>\",\n \"TimeZoneSidKey\": \"America/Los_Angeles\",\n \"LocaleSidKey\": \"en_US\",\n \"EmailEncodingKey\": \"UTF-8\",\n \"LanguageLocaleKey\": \"en_US\",\n \"UserPermissionsKnowledgeUser\": true\n }\n ```\n\n3. Create the user:\n\n **Option A: Scratch Org (Definition File)**\n ```bash\n sf org create user \\\n --definition-file config/einstein-agent-user.json \\\n -o TARGET_ORG\n ```\n\n **Option B: Production/Sandbox (Direct Record Creation)**\n ```bash\n # Get Profile ID first\n PROFILE_ID=$(sf data query \\\n --query \"SELECT Id FROM Profile WHERE Name = 'Einstein Agent User'\" \\\n -o TARGET_ORG --json | jq -r '.result.records[0].Id')\n\n # Create user directly\n sf data create record --sobject User --values \\\n \"Username='{agent_name}_agent@{orgId}.ext' LastName='{AgentName} Agent' Email='[email protected]' Alias='agntuser' ProfileId='${PROFILE_ID}' TimeZoneSidKey='America/Los_Angeles' LocaleSidKey='en_US' EmailEncodingKey='UTF-8' LanguageLocaleKey='en_US'\" \\\n -o TARGET_ORG --json\n ```\n\n > **`sf org create user` only works in scratch orgs.** For production/sandbox, use `sf data create record`. Attempting `sf org create user` in a non-scratch org fails with an authorization error.\n\n4. Verify creation:\n ```bash\n sf data query --query \"SELECT Id, Username, IsActive FROM User WHERE Username = '{agent_name}_agent@{orgId}.ext'\" -o TARGET_ORG --json\n ```\n\n> **Username format**: `{agent_name}_agent@{orgId}.ext` (production) or `{agent_name}.{suffix}@{orgfarm}.salesforce.com` (dev/scratch). Always query the target org to confirm the exact format.\n\n---\n\n### Step 2: Assign Native System Permission Sets\n\n**CRITICAL**: If you are not using `sf org create agent-user`, you must mirror the native system assignments BEFORE publishing the agent. Without them, publish or runtime execution can fail with generic errors.\n\n**Assign these system permission sets:**\n- `AgentforceServiceAgentBase`\n- `AgentforceServiceAgentUser`\n- `EinsteinGPTPromptTemplateUser`\n\n**Via Setup UI:**\n1. Setup > Permission Sets > assign `AgentforceServiceAgentBase`\n2. Setup > Permission Sets > assign `AgentforceServiceAgentUser`\n3. Setup > Permission Sets > assign `EinsteinGPTPromptTemplateUser`\n\n**Via CLI:**\n```bash\nsf org assign permset --name AgentforceServiceAgentBase --on-behalf-of \"{agent_name}_agent@{orgId}.ext\" -o TARGET_ORG --json\nsf org assign permset --name AgentforceServiceAgentUser --on-behalf-of \"{agent_name}_agent@{orgId}.ext\" -o TARGET_ORG --json\nsf org assign permset --name EinsteinGPTPromptTemplateUser --on-behalf-of \"{agent_name}_agent@{orgId}.ext\" -o TARGET_ORG --json\n```\n\n**Verify assignments:**\n```bash\nsf data query --query \"SELECT Id, PermissionSet.Name FROM PermissionSetAssignment WHERE Assignee.Username = '{agent_name}_agent@{orgId}.ext' AND PermissionSet.Name IN ('AgentforceServiceAgentBase', 'AgentforceServiceAgentUser', 'EinsteinGPTPromptTemplateUser')\" -o TARGET_ORG --json\n```\n\n---\n\n### Step 3: Create Custom Permission Set for Apex Classes\n\nThe custom PS grants the agent user permission to execute your Apex invocable actions.\n\n**Naming convention**: `{AgentName}_Access` (e.g., `CustomerSupport_Access`)\n\n**File**: `force-app/main/default/permissionsets/{AgentName}_Access.permissionset-meta.xml`\n\n```xml\n\u003c?xml version=\"1.0\" encoding=\"UTF-8\"?>\n\u003cPermissionSet xmlns=\"http://soap.sforce.com/2006/04/metadata\">\n \u003cdescription>Grants access to {AgentName} Agent Apex classes\u003c/description>\n \u003chasActivationRequired>false\u003c/hasActivationRequired>\n \u003clabel>{AgentName} Access\u003c/label>\n\n \u003c!-- Add one entry per Apex class the agent calls -->\n \u003cclassAccesses>\n \u003capexClass>YourApexClassName\u003c/apexClass>\n \u003cenabled>true\u003c/enabled>\n \u003c/classAccesses>\n \u003c!-- Repeat for ALL Apex classes referenced via apex:// in agent script -->\n\u003c/PermissionSet>\n```\n\n**Key rule**: Include EVERY Apex class referenced via `apex://` in your agent script. Missing even one causes \"invocable action does not exist\" at runtime.\n\n**Deploy the permission set:**\n```bash\nsf project deploy start --source-dir force-app/main/default/permissionsets/{AgentName}_Access.permissionset-meta.xml -o TARGET_ORG --json\n```\n\n---\n\n### Step 4: Assign Custom Permission Set to Agent User\n\n**Via CLI:**\n```bash\nsf org assign permset --name {AgentName}_Access --on-behalf-of \"{agent_name}_agent@{orgId}.ext\" -o TARGET_ORG --json\n```\n\n**Verify both permission sets are assigned:**\n```bash\nsf data query --query \"SELECT PermissionSet.Name FROM PermissionSetAssignment WHERE Assignee.Username = '{agent_name}_agent@{orgId}.ext'\" -o TARGET_ORG --json\n```\n\nExpected output should include all of these:\n- `AgentforceServiceAgentBase` (system)\n- `AgentforceServiceAgentUser` (system)\n- `EinsteinGPTPromptTemplateUser` (system)\n- `{AgentName}_Access` (custom)\n\n---\n\n### Step 5: Set `default_agent_user` in Agent Config\n\nIn your `.agent` file:\n```yaml\nconfig:\n developer_name: \"AgentName\"\n description: \"Your agent description\"\n agent_type: \"AgentforceServiceAgent\"\n default_agent_user: \"{agent_name}_agent@{orgId}.ext\" # Service agents ONLY\n```\n\n> Official docs may describe `default_agent_user` as an API name or ID. In this skill, the standard operational path is to store the concrete username returned by `sf org create agent-user`, because that is the easiest value to verify in org queries and publish troubleshooting.\n\n**Before publishing, verify the actual user object** — not just the username string:\n\n```bash\nsf data query --query \"\nSELECT Username, IsActive, UserType, Profile.Name\nFROM User\nWHERE Username = '{agent_name}_agent@{orgId}.ext'\nLIMIT 1\n\" -o TARGET_ORG --json\n```\n\nA valid Service Agent user must satisfy all of these:\n- user exists\n- `IsActive = true`\n- `UserType != AutomatedProcess`\n- `Profile.Name = 'Einstein Agent User'`\n\nThis catches cases where `sf agent validate` passes but `sf agent publish` later fails because the configured user is missing, inactive, `AutomatedProcess`, or not on the **Einstein Agent User** profile.\n\n**Recommended native sequence:**\n1. `sf agent validate authoring-bundle --api-name \u003cAgentName> -o TARGET_ORG --json`\n2. Run the exact `sf data query` above for `default_agent_user`\n3. Smoke-test with `sf agent preview start` / `send` / `end`\n4. Publish with `sf agent publish authoring-bundle`\n5. If publish fails after validate + preview pass, retry with `--skip-retrieve`\n\n---\n\n### Step 6: Deploy, Test, Publish & Activate\n\n> **Validated workflow pattern**: Deploy as unpublished metadata, test with preview, then publish only when tests pass. This avoids version management overhead during iteration.\n\n#### 6.1: Deploy Agent Bundle (Unpublished)\n\n```bash\nsf project deploy start \\\n --source-dir force-app/main/default/aiAuthoringBundles/\u003cAgentName> \\\n -o TARGET_ORG --json\n```\n\nThis deploys the agent as **unpublished metadata** — you can edit freely without version management.\n\n#### 6.2: Test with Preview (Before Publishing)\n\n```bash\nSESSION_ID=$(sf agent preview start \\\n --authoring-bundle \u003cAgentName> \\\n --use-live-actions \\\n -o TARGET_ORG --json | jq -r '.result.sessionId')\n\nsf agent preview send \\\n --session-id \"$SESSION_ID\" \\\n --authoring-bundle \u003cAgentName> \\\n --utterance \"hello\" \\\n -o TARGET_ORG --json\n\nsf agent preview end \\\n --session-id \"$SESSION_ID\" \\\n --authoring-bundle \u003cAgentName> \\\n -o TARGET_ORG --json\n```\n\n**What to test:**\n1. All topics trigger correctly\n2. All Apex actions execute without \"Insufficient Privileges\" errors\n3. Agent responds with expected data\n4. No compilation errors\n\nIf testing reveals problems, edit your agent script or Apex classes, redeploy, and test again — no publish required.\n\n> See [preview-test-loop.md](preview-test-loop.md) for the complete smoke test workflow with `jq` trace analysis recipes.\n\n#### 6.3: Publish Agent\n\n**Only publish after all tests pass.**\n\n```bash\n# First try the standard publish path\nsf agent publish authoring-bundle \\\n --api-name \u003cAgentName> \\\n -o TARGET_ORG --json\n\n# If that fails after validate/preview pass, retry without retrieve-back\nsf agent publish authoring-bundle \\\n --api-name \u003cAgentName> \\\n -o TARGET_ORG --skip-retrieve --json\n```\n\n> **Publishing does NOT activate.** The new BotVersion is created as `Inactive`. You must explicitly activate.\n>\n> **Why `--skip-retrieve` matters**: In org-backed testing, some publishes succeeded in the org but the CLI failed in the retrieve/deploy-back phase. `--skip-retrieve` isolates that tooling issue.\n\n#### 6.4: Activate Agent\n\n```bash\n# Manual activation\nsf agent activate \\\n --api-name \u003cAgentName> \\\n -o TARGET_ORG\n\n# CI / deterministic activation of a known BotVersion\nsf agent activate \\\n --api-name \u003cAgentName> \\\n --version \u003cn> \\\n -o TARGET_ORG --json\n```\n\n> `sf agent activate` now supports `--json`.\n> If you use `--json` without `--version`, the CLI activates the latest agent version. Prefer `--version` for CI/CD and reproducible rollouts.\n\n#### 6.5: Verify Activation\n\n```bash\nsf data query \\\n --query \"SELECT Id, DeveloperName, Status FROM BotVersion WHERE BotDefinition.DeveloperName = '\u003cAgentName>' ORDER BY CreatedDate DESC LIMIT 1\" \\\n -o TARGET_ORG --json\n```\n\n**Expected:** `Status = 'Active'`\n\n**After publish:** Any further changes require version management. Test thoroughly before publishing.\n\n---\n\n## Employee Agent Setup\n\nEmployee agents run as the logged-in user. The permission model is simpler.\n\n### What You DO NOT Need\n\n- No Einstein Agent User creation\n- No native Service Agent system permission sets (`AgentforceServiceAgentBase`, `AgentforceServiceAgentUser`, `EinsteinGPTPromptTemplateUser`)\n- No `default_agent_user` in agent config\n\n### What You DO Need\n\nCustom permission set(s) assigned to **employees** who will use the agent.\n\n### Step 1: Create Custom Permission Set\n\nSame XML template as Step 3 above. Include `\u003cclassAccesses>` for all Apex classes the agent calls.\n\n### Step 2: Assign to Employees\n\nAssign the custom PS to employees (not to a service account):\n\n```bash\nsf org assign permset --name {AgentName}_Access --on-behalf-of \"[email protected]\" -o TARGET_ORG --json\n```\n\nOr use Permission Set Groups for role-based access.\n\n### Step 3: Configure Agent Script (No `default_agent_user`)\n\n```yaml\nconfig:\n developer_name: \"Employee_Agent\"\n description: \"Internal employee assistant\"\n agent_type: \"AgentforceEmployeeAgent\"\n # NO default_agent_user — agent runs as logged-in user\n```\n\n### Step 4: Publish\n\n```bash\nsf agent publish authoring-bundle --api-name Employee_Agent -o TARGET_ORG --json\n```\n\n### Common Mistakes with Employee Agents\n\n| Mistake | Symptom | Fix |\n|---------|---------|-----|\n| Including `default_agent_user` | HTTP 500 on publish | Remove the field entirely |\n| Omitting `agent_type` | Defaults to Service Agent, then fails if no agent user | Set `agent_type: \"AgentforceEmployeeAgent\"` explicitly |\n| Including Messaging-linked variables | Unnecessary bloat, may cause errors if Messaging not configured | Remove `EndUserId`, `RoutableId`, `ContactId` linked vars |\n| Including `connection messaging:` block | Service Agent only -- causes errors for Employee Agent | Remove the entire connection block |\n| Including `language:` block | Not required for Employee agents | Optional -- remove to keep minimal |\n\n---\n\n## Auto-Generated Permission Set Warning\n\nSalesforce auto-generates `NextGen_{AgentName}_Permissions` when an agent is published. **Do NOT rely on this PS.** It is often incomplete.\n\n**Representative example:**\n- Agent script referenced 4 Apex classes: `IdentityVerificationService`, `RiskScoringService`, `OrderLookupService`, `ShipmentTrackingService`\n- Auto-generated `NextGen_Support_Agent_Permissions` only included 3 classes (missing `ShipmentTrackingService`)\n- Runtime error: \"invocable action track_delivery does not exist\"\n- Fix: Created custom `Support_Agent_Access` with all 4 classes — no errors\n\n**Best practice**: Always create your own custom `{AgentName}_Access` PS with explicit `\u003cclassAccesses>` for every Apex class. Ignore the auto-generated PS.\n\n---\n\n## End-to-End Verification Checklist\n\nRun this combined query to verify all setup steps for a Service Agent:\n\n```bash\n# 1. Einstein Agent User exists and is active\nsf data query --query \"SELECT Id, Username, IsActive, Profile.Name FROM User WHERE Username = '{agent_name}_agent@{orgId}.ext'\" -o TARGET_ORG --json\n\n# 2. Native system PS assigned\nsf data query --query \"SELECT PermissionSet.Name FROM PermissionSetAssignment WHERE Assignee.Username = '{agent_name}_agent@{orgId}.ext' AND PermissionSet.Name IN ('AgentforceServiceAgentBase', 'AgentforceServiceAgentUser', 'EinsteinGPTPromptTemplateUser')\" -o TARGET_ORG --json\n\n# 3. Custom PS assigned\nsf data query --query \"SELECT PermissionSet.Name FROM PermissionSetAssignment WHERE Assignee.Username = '{agent_name}_agent@{orgId}.ext' AND PermissionSet.Name = '{AgentName}_Access'\" -o TARGET_ORG --json\n\n# 4. All permission sets for user (combined view)\nsf data query --query \"SELECT PermissionSet.Name, PermissionSet.Label FROM PermissionSetAssignment WHERE Assignee.Username = '{agent_name}_agent@{orgId}.ext'\" -o TARGET_ORG --json\n\n# 5. Agent config has default_agent_user\n# Check your .agent file's config: block\n\n# 6. Agent publishes successfully\nsf agent publish authoring-bundle --api-name AgentName -o TARGET_ORG --json\n```\n\n**Checklist:**\n- [ ] Einstein Agent User created and active (`IsActive = true`)\n- [ ] Profile is \"Einstein Agent User\" (or \"Minimum Access - Salesforce\")\n- [ ] `AgentforceServiceAgentBase`, `AgentforceServiceAgentUser`, and `EinsteinGPTPromptTemplateUser` system PS assigned\n- [ ] Custom `{AgentName}_Access` PS deployed with ALL Apex classes\n- [ ] Custom PS assigned to the agent user\n- [ ] `default_agent_user` set in `.agent` config block\n- [ ] Agent tested with preview before publishing\n- [ ] Agent publishes without error\n- [ ] Agent activated (publish does NOT auto-activate)\n\n---\n\n## Common Pitfalls (Validated)\n\n### 1. \"Internal Error\" on First Publish\n- **Cause:** Publishing before the native Service Agent system assignments are in place\n- **Prevention:** Prefer `sf org create agent-user`, or manually assign all Step 2 system permission sets before publishing (Step 6.3)\n- **Result:** First-time publish success (no retries needed)\n\n### 2. \"Insufficient Privileges\" on Apex Actions\n- **Cause:** Missing `\u003cclassAccesses>` in custom permission set\n- **Prevention:** Custom PS template includes all Apex classes (Step 3)\n- **Result:** All actions execute without permission errors\n\n### 3. Testing After Publishing\n- **Cause:** Publishing before testing, then needing version management for fixes\n- **Prevention:** Deploy → Test → Publish workflow (Step 6.1-6.3)\n- **Result:** No version management overhead during development\n\n### 4. Wrong User Creation Command\n- **Cause:** Using `sf org create user` in non-scratch orgs\n- **Prevention:** Step 1 provides correct commands for each org type (Option A vs B)\n- **Result:** User created successfully without authorization errors\n\n### 5. Auto-Generated Permission Set Gaps\n- **Cause:** Relying on `NextGen_{AgentName}_Permissions` (often incomplete)\n- **Prevention:** Custom PS with explicit Apex access (Step 3)\n- **Result:** All Apex classes accessible from the start\n\n### 6. Forgot to Activate After Publish\n- **Cause:** Assuming publish automatically activates\n- **Prevention:** Step 6 splits publish and activate into separate steps with verification\n- **Result:** Agent is both published AND activated\n\n---\n\n## Troubleshooting\n\n| Error | Cause | Fix |\n|-------|-------|-----|\n| \"Internal Error\" on publish | One or more native Service Agent system permission sets missing | Prefer `sf org create agent-user`, or assign Step 2 system PS manually, wait 2-3 min, retry publish |\n| \"Insufficient Privileges\" at runtime | Custom PS missing or incomplete `\u003cclassAccesses>` | Verify custom PS includes ALL Apex classes, redeploy + reassign |\n| \"invocable action does not exist\" | Apex class not in custom PS (auto-generated PS incomplete) | Create custom `{AgentName}_Access` with all `\u003cclassAccesses>` (Step 3) |\n| \"Invalid default_agent_user\" | Username typo or user not active | Query Einstein Agent Users, verify exact username + `IsActive = true` |\n| Agent runs but returns wrong data | Employee agent using wrong user context | Verify `agent_type` — Service agents use dedicated user, Employee agents use logged-in user |\n| `sf org create user` fails | Used in production/sandbox org | Use `sf data create record` instead (Step 1, Option B) |\n| Native system behavior differs from local assumptions | Running user was created manually instead of with the native command | Re-check with `sf org create agent-user --help` and verify `AgentforceServiceAgentBase`, `AgentforceServiceAgentUser`, and `EinsteinGPTPromptTemplateUser` assignments |\n\n---\n\n## Permission Set XML Template (Complete Example)\n\n**CustomerSupport agent** (5 Apex classes):\n\n```xml\n\u003c?xml version=\"1.0\" encoding=\"UTF-8\"?>\n\u003cPermissionSet xmlns=\"http://soap.sforce.com/2006/04/metadata\">\n \u003cdescription>Grants access to Automotive Support Agent Apex classes\u003c/description>\n \u003chasActivationRequired>false\u003c/hasActivationRequired>\n \u003clabel>Automotive Support Access\u003c/label>\n\n \u003cclassAccesses>\n \u003capexClass>VehicleLookupService\u003c/apexClass>\n \u003cenabled>true\u003c/enabled>\n \u003c/classAccesses>\n \u003cclassAccesses>\n \u003capexClass>ErrorCodeDiagnosticsService\u003c/apexClass>\n \u003cenabled>true\u003c/enabled>\n \u003c/classAccesses>\n \u003cclassAccesses>\n \u003capexClass>CheckEngineDiagnosticsService\u003c/apexClass>\n \u003cenabled>true\u003c/enabled>\n \u003c/classAccesses>\n \u003cclassAccesses>\n \u003capexClass>BehaviorAnalysisService\u003c/apexClass>\n \u003cenabled>true\u003c/enabled>\n \u003c/classAccesses>\n \u003cclassAccesses>\n \u003capexClass>ServiceSchedulerService\u003c/apexClass>\n \u003cenabled>true\u003c/enabled>\n \u003c/classAccesses>\n\u003c/PermissionSet>\n```\n\n---\n\n*Validated against representative Service Agent and Employee Agent scenarios. Last validated: 2026-03-07.*\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":23522,"content_sha256":"676aa54f9069d5e914fdbbb610e8f2a2814447567de99af23c4e0441a279b383"},{"filename":"references/architecture-patterns.md","content":"\u003c!-- Parent: sf-ai-agentscript/SKILL.md -->\n\n# Architecture Patterns\n\nUse these patterns when deciding how much should be deterministic, how much should be LLM-directed, and how to split work across topics.\n\n---\n\n## Control Heuristic\n\n- Start with the minimum deterministic control needed.\n- Script only where “mostly right” is unacceptable.\n- Keep high-variance conversation LLM-directed when possible.\n- If the workflow has no real reasoning need, use Flow or Apex.\n\n---\n\n## Pattern 0: Deterministic Sandwich\n\nApply hard control at the edges and leave the middle flexible.\n\n```text\ngate / triage / auth → flexible worker topic(s) → deterministic closeout\n```\n\n**Use when:**\n- auth or verification must happen first\n- compliance text or closeout steps must always run\n- the middle still benefits from flexible reasoning\n\n**Anti-pattern:** scripting the whole conversation when only the edges need guarantees. If the middle is also fixed, use Flow or Apex.\n\n---\n\n## Pattern 1: Hub and Spoke\n\nCentral router (hub) to specialized topics (spokes). Use for multi-purpose agents.\n\n```\n ┌─────────────┐\n │ router │\n │ (hub) │\n └──────┬──────┘\n ┌─────────┼─────────┐\n ▼ ▼ ▼\n┌────────┐ ┌────────┐ ┌────────┐\n│refunds │ │ orders │ │support │\n└────────┘ └────────┘ └────────┘\n```\n\n**Use when:** the first turn is mostly classification and each downstream topic owns a distinct capability.\n\n**Routing note:** keep spoke descriptions semantically non-overlapping. If one utterance plausibly fits two spokes, merge, split, or rename them.\n\n---\n\n## Pattern 2: Verification Gate\n\nSecurity gate before protected topics. Mandatory for sensitive data.\n\n```\n┌─────────┐ ┌──────────┐ ┌───────────┐\n│ entry │ ──▶ │ VERIFY │ ──▶ │ protected │\n└─────────┘ │ (GATE) │ │ topics │\n └────┬─────┘ └───────────┘\n │ 3 fails\n ▼\n ┌──────────┐\n │ lockout │\n └──────────┘\n```\n\n**Use when:** account access, protected updates, regulated data, or irreversible actions require a hard precondition.\n\n---\n\n## Pattern 3: Post-Action Loop\n\nTopic re-resolves after action completes — put completion checks at the **top**.\n\n```yaml\ntopic refund:\n reasoning:\n instructions: ->\n # POST-ACTION CHECK (at TOP - triggers on next loop)\n if @variables.refund_status == \"Approved\":\n run @actions.create_crm_case\n transition to @topic.success\n\n # PRE-LLM DATA LOADING\n run @actions.check_churn_risk\n set @variables.risk = @outputs.score\n\n # DYNAMIC INSTRUCTIONS FOR LLM\n if @variables.risk >= 80:\n | Offer full refund to retain customer.\n else:\n | Offer $10 credit instead.\n```\n\n**Use when:** an action updates state and the next deterministic branch depends on that updated state.\n\n---\n\n## Pattern 4: Staged Multi-Turn Workflow\n\nWhen a workflow naturally spans **multiple user turns**, model each phase as its own topic instead of keeping a single topic with `step = 1 / 2 / 3` flags.\n\n```yaml\nvariables:\n lookup_id: mutable string = \"\"\n lookup_result: mutable string = \"\"\n\ntopic collect_lookup_id:\n description: \"Collect the identifier needed for the lookup\"\n reasoning:\n instructions: ->\n if @variables.lookup_id == \"\":\n | Ask the user for the identifier, then call {[email protected]_lookup_id}.\n else:\n transition to @topic.run_lookup\n actions:\n save_lookup_id: @utils.setVariables\n description: \"Save the identifier for the next stage\"\n with lookup_id = ...\n\ntopic run_lookup:\n description: \"Run the lookup after the identifier is known\"\n reasoning:\n instructions: ->\n if @variables.lookup_result != \"\":\n transition to @topic.present_lookup_result\n\n run @actions.lookup_record\n with lookup_id = @variables.lookup_id\n set @variables.lookup_result = @outputs.result\n\ntopic present_lookup_result:\n description: \"Present the lookup result\"\n reasoning:\n instructions: |\n Share {[email protected]_result} with the user.\n```\n\n**Why this pattern is safer:**\n- user input capture stays LLM-directed\n- deterministic work only runs after required state exists\n- each turn boundary is explicit\n- transitions are easier to debug than step counters\n\n**Anti-pattern to avoid:** one large topic that increments a `step` variable and accidentally triggers step 2 in the same turn that finished step 1.\n\n---\n\n## Pattern 5: Multi-Intent Orchestration\n\nIf a user can ask about **more than one independent intent in the same utterance**, decide the architecture based on the desired response style.\n\n| Scenario | Recommended pattern |\n|---|---|\n| User may ask about several independent tasks in one message | Expose specialist topics/capabilities as delegated tools and let the orchestrator route between them |\n| One business process spans multiple turns in sequence | Use staged topics with explicit transitions |\n| Only the final stage should produce the user-facing answer | Use intermediate work topics, then transition to a final presentation topic |\n\n**Lightweight router example:**\n\n```yaml\nstart_agent router:\n description: \"Route to the right specialist\"\n reasoning:\n instructions: |\n Determine which specialist capability is needed.\n actions:\n handle_shipping: @topic.shipping_status\n description: \"Handle shipping status questions\"\n handle_returns: @topic.returns\n description: \"Handle return questions\"\n handle_billing: @topic.billing\n description: \"Handle billing questions\"\n```\n\n**Guidance:**\n- For genuine multi-intent handling, prefer specialist tools/topics over building a queue string in one monolithic topic.\n- For single-intent chained workflows, prefer `after_reasoning` or explicit topic transitions.\n- If the orchestrator should not answer directly, let worker topics do the work and transition to one final response topic.\n\n---\n\n## Quick Decision Guide\n\n| Need | Prefer |\n|---|---|\n| Hard gating before sensitive actions | Verification Gate |\n| Critical gate + flexible middle + must-run closeout | Deterministic Sandwich |\n| Deterministic branch after action result | Post-Action Loop |\n| Multi-turn data collection then deterministic execution | Staged Multi-Turn Workflow |\n| Multiple specialist capabilities | Hub and Spoke |\n| Several possible intents in one utterance | Multi-Intent Orchestration |\n| Fully fixed workflow with no real reasoning need | Flow or Apex instead of full Agent Script |\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":7060,"content_sha256":"183c6e1ee75e1e00f808c3a92fcb735b9a631fdb20bedc8145c14ae2266c83be"},{"filename":"references/asset-validation-profiles.md","content":"# Asset Validation Profiles\n\nThe `assets/` directory intentionally mixes **three kinds of `.agent` files**:\n\n1. **Standalone examples/templates** — complete files that should validate as full agents.\n2. **Org fixtures** — structurally valid files that may block only because the currently resolved org is missing referenced users/Flows/classes.\n3. **Partial snippets/patterns** — reusable building blocks that intentionally omit top-level blocks or rely on surrounding context.\n\nBecause of that mix, a single \"every `.agent` file must have 0 blocking findings\" rule creates false failures.\n\n## Profile Manifest\n\nSource of truth:\n\n- `assets/validation-profiles.json`\n\nValidation harness:\n\n- `scripts/validate-asset-profiles.py`\n\n## How the Harness Works\n\nThe harness runs the main validator (`hooks/scripts/agentscript-syntax-validator.py`) against every `assets/**/*.agent` file, then checks the results against the profile manifest.\n\n### Profile types\n\n#### 1. `standalone_examples`\nThese are expected to be complete agents/templates.\n\nPolicy:\n- **No blocking findings allowed**\n- Only explicitly allowlisted warning IDs may appear\n- Typical intentional warning classes here are template/demo reminders (`ASV-CFG-007`) and teaching-pattern advisories such as planner-hint warnings (`ASV-RUN-007`)\n\n#### 2. `org_fixtures`\nThese are complete files, but the current local org may not contain all referenced metadata.\n\nPolicy:\n- Only explicitly allowlisted org-dependent blocking IDs may appear\n- Only explicitly allowlisted warning IDs may appear\n- Current example: `ASV-ORG-007` for missing outbound route Flows in the resolved org\n\n#### 3. `partial_snippets`\nThese are partial fragments or pattern files that are not intended to be published directly.\n\nPolicy:\n- Only explicitly allowlisted completeness/snippet-related blocking IDs may appear\n- Only explicitly allowlisted warning IDs may appear\n- Any new unexpected blocker **or warning** still fails the harness\n\n## Run It\n\n```bash\npython3 skills/sf-ai-agentscript/scripts/validate-asset-profiles.py\n```\n\nExpected success shape:\n\n```text\nAsset validation profile summary\n-------------------------------\n- standalone_examples: ...\n- org_fixtures: ...\n- partial_snippets: ...\n\n✅ All asset files matched their configured validation profile expectations.\n```\n\n## Why This Is Better Than Ignoring Failures\n\nThis profile-based approach keeps the validator strict **without** pretending every asset file is the same kind of artifact.\n\nIt gives us:\n- strict validation for complete examples\n- explicit documentation of intentional partials\n- controlled tolerance for org-dependent fixtures\n- failure on newly introduced unexpected blockers\n- failure on newly introduced unexpected warnings that are not part of the file/profile's documented teaching intent\n\n## Maintenance Rules\n\nWhen you add a new file under `assets/`:\n\n1. Decide whether it is a **standalone example**, **org fixture**, or **partial snippet**.\n2. Add it to `assets/validation-profiles.json`.\n3. If you allow blocking IDs or warning IDs for it, document *why* in the PR or commit.\n4. Re-run:\n ```bash\n python3 skills/sf-ai-agentscript/scripts/validate-asset-profiles.py\n ```\n\nIf the harness fails, either:\n- fix the asset, or\n- update the profile manifest intentionally.\n\nDo **not** silently broaden allowlists without explaining why.\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":3362,"content_sha256":"0a05913bcee17fb7b2393718c91e3123e334815b1aa5af79633d5dbe2d306e26"},{"filename":"references/cli-guide.md","content":"\u003c!-- Parent: sf-ai-agentscript/SKILL.md -->\n# Agent Script CLI Quick Reference\n\n> Pro-Code Lifecycle: Git, CI/CD, and CLI for Agent Development\n\n---\n\n## The sf agent Commands\n\n| Command | Purpose | Example |\n|---------|---------|---------|\n| `sf project retrieve start` | Pull agent from org | `sf project retrieve start --metadata Agent:MyAgent --target-org sandbox` |\n| `sf org create agent-user` | Create native Service Agent running user | `sf org create agent-user --target-org TARGET_ORG --json` |\n| `sf agent validate authoring-bundle` | Check syntax before deploy | `sf agent validate authoring-bundle --api-name MyAgent -o TARGET_ORG` |\n| `sf agent publish authoring-bundle` | Publish agent to org | `sf agent publish authoring-bundle --api-name MyAgent -o TARGET_ORG --json` |\n| `sf agent test run` | Run batch tests | `sf agent test run --api-name MyTestDef --wait 10 -o TARGET_ORG --json` |\n| `sf agent create` | Create non-Agent Script agent from spec file (legacy / not default for this skill) | `sf agent create --name \"My Agent\" --api-name MyAgent --spec agent-spec.yaml -o TARGET_ORG --json` |\n| `sf agent generate agent-spec` | Generate agent specification | `sf agent generate agent-spec --type customer --role \"Service Rep\" --output-file agent-spec.yaml` |\n| `sf agent generate authoring-bundle` | Scaffold authoring bundle | `sf agent generate authoring-bundle --no-spec --name \"My Agent\" -o TARGET_ORG --json` |\n| `sf agent activate` | Activate agent (make live) | `sf agent activate --api-name MyAgent --version \u003cn> -o TARGET_ORG --json` |\n| `sf agent deactivate` | Deactivate agent (take offline) | `sf agent deactivate --api-name MyAgent -o TARGET_ORG --json` |\n| `sf agent preview start` | Start programmatic preview session | `sf agent preview start --authoring-bundle MyAgent --simulate-actions -o TARGET_ORG --json` (or `--api-name MyAgent`) |\n| `sf agent preview send` | Send utterance to preview session | `sf agent preview send --session-id \u003cid> --authoring-bundle MyAgent --utterance \"Hello\" -o TARGET_ORG --json` |\n| `sf agent preview end` | End preview session | `sf agent preview end --session-id \u003cid> --authoring-bundle MyAgent -o TARGET_ORG --json` |\n| `sf org open agent` | Open Agent Builder in browser | `sf org open agent --api-name MyAgent -o TARGET_ORG` |\n| `sf org open authoring-bundle` | Open Agentforce Studio list view | `sf org open authoring-bundle -o TARGET_ORG` |\n\n> ⚠️ **CRITICAL**: Use `sf agent publish authoring-bundle` for Agent Script deployment, NOT `sf project deploy start`. The metadata API deploy will fail with \"Required fields are missing: [BundleType]\".\n>\n> Default workflow for this skill: edit or generate the `.agent` bundle directly. Use `sf agent generate agent-spec` only when the user explicitly wants LLM-assisted topic ideation.\n>\n> ℹ️ **Managed packaging note:** `sf agent generate template` targets Bot / BotVersion metadata and is **not** a packaging path for Agent Script `.agent` bundles. See [known-issues.md](known-issues.md) for the current Agent Script packaging limitation.\n\n---\n\n## Create Service Agent User (GA)\n\n```bash\nsf org create agent-user --target-org TARGET_ORG --json\nsf org create agent-user --first-name Service --last-name Agent --target-org TARGET_ORG --json\nsf org create agent-user --base-username [email protected] --target-org TARGET_ORG --json\n```\n\nThe native command assigns the standard Service Agent profile and system permission sets automatically. Use the returned username as the `default_agent_user` value.\n\n## Authoring Bundle Structure\n\n> ⚠️ **CRITICAL NAMING CONVENTION**: File must be named `AgentName.bundle-meta.xml`, NOT `AgentName.aiAuthoringBundle-meta.xml`. The metadata API expects `.bundle-meta.xml` suffix.\n\n```\nforce-app/main/default/aiAuthoringBundles/\n└── MyAgent/\n ├── MyAgent.agent # Your Agent Script (REQUIRED)\n └── MyAgent.bundle-meta.xml # Metadata XML (REQUIRED)\n```\n\n### AgentName.bundle-meta.xml Content\n\n```xml\n\u003c?xml version=\"1.0\" encoding=\"UTF-8\"?>\n\u003cAiAuthoringBundle xmlns=\"http://soap.sforce.com/2006/04/metadata\">\n \u003cbundleType>AGENT\u003c/bundleType>\n\u003c/AiAuthoringBundle>\n```\n\n> ⚠️ **COMMON ERROR**: Using `\u003cBundleType>` (PascalCase) instead of `\u003cbundleType>` (camelCase) will NOT cause errors, but the field name in the XML element is `bundleType` (lowercase b).\n\n### Bundle Naming Rules\n\n| Component | Convention | Example |\n|-----------|------------|---------|\n| Folder name | PascalCase or snake_case | `MyAgent/` or `My_Agent/` |\n| Agent script | Same as folder + `.agent` | `MyAgent.agent` |\n| Metadata XML | Same as folder + `.bundle-meta.xml` | `MyAgent.bundle-meta.xml` |\n\n### Deployment Command (NOT sf project deploy!)\n\n```bash\n# ✅ CORRECT: Use sf agent publish authoring-bundle\nsf agent publish authoring-bundle --api-name MyAgent -o TARGET_ORG\n\n# ❌ WRONG: Do NOT use sf project deploy start\n# This will fail with \"Required fields are missing: [BundleType]\"\n```\n\n---\n\n## Pro-Code Workflow\n\n```\n┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐\n│ 1 Retrieve │ → │ 2 Edit │ → │ 3 Validate │ → │ 4 Deploy │\n│ Pull agent │ │ CLI/editor │ │ Check syntax│ │ Push to prod│\n│ from org │ │ + Claude │ │ │ │ │\n└─────────────┘ └─────────────┘ └─────────────┘ └─────────────┘\n```\n\n### Step 1: Retrieve\n\n```bash\n# Retrieve from sandbox\nsf project retrieve start --metadata Agent:MyAgent --target-org sandbox --json\n```\n\n> **⚠️ Flow Version Mismatch Warning**: `sf project retrieve start --metadata Flow:FlowName` retrieves the **latest** Flow version, which may be Draft or Obsolete — NOT necessarily the active version. The Agent Script publisher validates action I/O against the **active** Flow version's inputs/outputs. If the latest version has different I/O than the active version (e.g., a renamed input parameter), your action definition will compile locally but fail on publish.\n>\n> **How to verify the active Flow version's inputs:**\n> ```bash\n> # 1. Find the active version ID\n> sf data query --query \"SELECT ActiveVersionId, LatestVersionId FROM FlowDefinitionView WHERE ApiName = 'My_Flow_Name' LIMIT 1\" -o TARGET_ORG --json\n>\n> # 2. If ActiveVersionId != LatestVersionId, inspect the active version:\n> sf data query --query \"SELECT Id, VersionNumber, Status FROM FlowVersionView WHERE FlowDefinitionViewId = 'DEFINITION_ID' AND Status = 'Active'\" -o TARGET_ORG --json\n> ```\n> Always define your action `inputs:` / `outputs:` to match the **active** Flow version, not the latest retrieved metadata.\n\n### Step 2: Edit\n\n```bash\n# Edit the agent script\nvim force-app/main/default/aiAuthoringBundles/MyAgent/MyAgent.agent\n```\n\n### Step 3: Validate\n\n```bash\n# Validate authoring bundle syntax\nsf agent validate authoring-bundle --api-name MyAgent -o TARGET_ORG --json\n```\n\n### Step 4: Publish\n\n```bash\n# Publish agent to org (4-step process: Validate → Publish → Retrieve → Deploy)\nsf agent publish authoring-bundle --api-name MyAgent -o TARGET_ORG --json\n\n# If publish fails AFTER validate/preview pass, retry without retrieve-back:\nsf agent publish authoring-bundle --api-name MyAgent -o TARGET_ORG --skip-retrieve --json\n\n# Expected output:\n# ✔ Validate Bundle ~1-2s\n# ✔ Publish Agent ~8-10s\n# ✔ Retrieve Metadata ~5-7s\n# ✔ Deploy Metadata ~4-6s\n```\n\n> ⚠️ Do NOT use `sf project deploy start` - it will fail with \"Required fields are missing: [BundleType]\"\n>\n> ⚠️ **Real-world failure split**:\n> - If plain publish fails but `--skip-retrieve` succeeds, the problem is usually the CLI retrieve/deploy-back phase, not Agent Script compilation.\n> - If publish still fails even with `--skip-retrieve`, suspect `default_agent_user` first.\n\n### Step 5: Activate\n\n> ⚠️ **Publishing does NOT activate.** The new BotVersion is created as `Inactive`. Tests, preview, and end users continue hitting the previously active version until you explicitly activate.\n\n```bash\n# Manual activation (choose interactively if you omit --version)\nsf agent activate --api-name MyAgent -o TARGET_ORG\n\n# CI / deterministic activation of a known BotVersion\nsf agent activate --api-name MyAgent --version \u003cn> -o TARGET_ORG --json\n\n# Verify activation (optional)\nsf data query --query \"SELECT DeveloperName, VersionNumber, Status FROM BotVersion WHERE BotDefinition.DeveloperName = 'MyAgent' AND Status = 'Active' LIMIT 1\" -o TARGET_ORG --json\n```\n\n> ℹ️ `sf agent activate` and `sf agent deactivate` now support `--json`.\n> If you use `--json` without `--version`, the CLI activates the latest agent version. Prefer `--version` for CI/CD and reproducible rollouts.\n\n---\n\n## Testing Commands\n\n```bash\n# Run agent tests (--api-name refers to an AiEvaluationDefinition, not the agent)\nsf agent test run --api-name MyTestDef --wait 10 -o TARGET_ORG --json\n```\n\n---\n\n## Validation Commands\n\n```bash\n# Validate authoring bundle syntax\nsf agent validate authoring-bundle --api-name MyAgent -o TARGET_ORG --json\n\n# Run tests against test definition\nsf agent test run --api-name MyTestDef --wait 10 -o TARGET_ORG --json\n```\n\n### Common Validation / Publish Errors\n\n| Error | Cause | Fix |\n|-------|-------|-----|\n| `Internal Error, try again later` during **publish** | Often invalid `default_agent_user` for Service Agent | Verify the user exists, is active, is NOT `AutomatedProcess`, and has Profile.Name = `Einstein Agent User` |\n| Plain publish fails, `--skip-retrieve` succeeds | CLI retrieve/deploy-back phase failed after successful publish | Re-run `sf agent publish authoring-bundle ... --skip-retrieve --json` |\n| `SyntaxError: You cannot mix spaces and tabs` | Mixed indentation | Use consistent spacing |\n| `Transition to undefined topic \"@topic.X\"` | Typo in topic name | Check spelling |\n| `Variables cannot be both mutable AND linked` | Conflicting modifiers | Choose one modifier |\n\n---\n\n## Einstein Agent User Setup (Service Agents Only)\n\n> This section applies to `AgentforceServiceAgent` only. `AgentforceEmployeeAgent` does NOT need an Einstein Agent User — it runs as the logged-in user. See [agent-user-setup.md](agent-user-setup.md) for the full provisioning workflow, CLI Fast Track, and Deploy → Test → Publish pattern.\n\n### Query Existing Users\n\n```bash\nsf data query --query \"SELECT Id, Username, IsActive FROM User WHERE Profile.Name = 'Einstein Agent User' AND IsActive = true\" -o TARGET_ORG --json\n```\n\n### Native Pre-Publish Check (Recommended)\n\n```bash\nsf data query --query \"\nSELECT Username, IsActive, UserType, Profile.Name\nFROM User\nWHERE Username = '[email protected]'\nLIMIT 1\n\" -o TARGET_ORG --json\n```\n\nUse this native CLI check before publish for Service Agents. A passing result must show:\n- a matching user record\n- `IsActive = true`\n- `UserType != AutomatedProcess`\n- `Profile.Name = 'Einstein Agent User'`\n\nThis covers the most common real-world publish failure: a `default_agent_user` value that passes `sf agent validate` but fails `sf agent publish` because the user is missing, inactive, `AutomatedProcess`, or not on the **Einstein Agent User** profile.\n\n### Native Pre-Publish Workflow\n\nUse this exact sequence for production-safe Service Agent publishes:\n\n```bash\n# 1. Static validation\nsf agent validate authoring-bundle --api-name MyAgent -o TARGET_ORG --json\n\n# 2. Verify the exact default_agent_user from the .agent config\nsf data query --query \"\nSELECT Username, IsActive, UserType, Profile.Name\nFROM User\nWHERE Username = '[email protected]'\nLIMIT 1\n\" -o TARGET_ORG --json\n\n# 3. Smoke-test the authoring bundle (authoring bundles now require an explicit mode)\nSESSION_ID=$(sf agent preview start --authoring-bundle MyAgent --simulate-actions -o TARGET_ORG --json | jq -r '.result.sessionId')\nsf agent preview send --session-id \"$SESSION_ID\" --authoring-bundle MyAgent --utterance \"hello\" -o TARGET_ORG --json\nsf agent preview end --session-id \"$SESSION_ID\" --authoring-bundle MyAgent -o TARGET_ORG --json\n\n# 4. Publish\nsf agent publish authoring-bundle --api-name MyAgent -o TARGET_ORG --json\n\n# 5. If plain publish fails after validate + preview pass, retry:\nsf agent publish authoring-bundle --api-name MyAgent -o TARGET_ORG --skip-retrieve --json\n```\n\nInterpretation:\n- Plain publish fails, `--skip-retrieve` works → retrieve/deploy-back phase issue in the CLI path\n- Both publish commands fail → inspect `default_agent_user`, action I/O, and other server-side publish constraints\n\n### Create Einstein Agent User\n\n**Option A: Scratch Org** (definition file):\n```bash\nsf org create user --definition-file config/einstein-agent-user.json -o TARGET_ORG\n```\n\n**Option B: Production/Sandbox** (direct record creation):\n```bash\nPROFILE_ID=$(sf data query \\\n --query \"SELECT Id FROM Profile WHERE Name = 'Einstein Agent User'\" \\\n -o TARGET_ORG --json | jq -r '.result.records[0].Id')\n\nsf data create record --sobject User --values \\\n \"Username='{agent_name}_agent@{orgId}.ext' LastName='{AgentName} Agent' Email='[email protected]' Alias='agntuser' ProfileId='${PROFILE_ID}' TimeZoneSidKey='America/Los_Angeles' LocaleSidKey='en_US' EmailEncodingKey='UTF-8' LanguageLocaleKey='en_US'\" \\\n -o TARGET_ORG --json\n```\n\n> **`sf org create user` only works in scratch orgs.** For production/sandbox, use `sf data create record`.\n\n### Username Format\n\n```\n{agent_name}_agent@\u003corg-id>.ext\n```\n\nExample: `[email protected]`\n\n### Get Org ID\n\n```bash\nsf org display -o TARGET_ORG --json | jq -r '.result.id'\n```\n\n### Verify Permission Set Assignments\n\nBoth the system PS and custom PS must be assigned to the Einstein Agent User:\n\n```bash\n# Check system PS\nsf data query --query \"SELECT PermissionSet.Name FROM PermissionSetAssignment WHERE Assignee.Username = '\u003cagent-username>' AND PermissionSet.Name = 'AgentforceServiceAgentUser'\" -o TARGET_ORG --json\n\n# Check custom PS\nsf data query --query \"SELECT PermissionSet.Name FROM PermissionSetAssignment WHERE Assignee.Username = '\u003cagent-username>' AND PermissionSet.Name = '{AgentName}_Access'\" -o TARGET_ORG --json\n\n# All assigned PSs\nsf data query --query \"SELECT PermissionSet.Name FROM PermissionSetAssignment WHERE Assignee.Username = '\u003cagent-username>'\" -o TARGET_ORG --json\n```\n\n### Recommended Workflow: Deploy → Test → Publish\n\nAfter user setup and permissions are configured, follow this order to avoid version management overhead:\n\n1. **Deploy** agent bundle as unpublished metadata\n2. **Test** with `sf agent preview start` — verify topics and actions\n3. **Publish** only after tests pass — `sf agent publish authoring-bundle`\n4. **Activate** separately — `sf agent activate` (publish does NOT auto-activate)\n\n> See [agent-user-setup.md](agent-user-setup.md) Step 6 for the full workflow with CLI commands.\n\n---\n\n## CI/CD Integration\n\n### GitHub Actions Example — 4-Step Deployment Pipeline\n\n```yaml\nname: Agent Deploy & Test\non: [push, pull_request]\njobs:\n deploy:\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v3\n\n # Step 1: Deploy supporting metadata (Flows, Apex, Objects)\n - name: Deploy Supporting Metadata\n run: sf project deploy start --source-dir force-app -o TARGET_ORG --json\n\n # Step 2: Validate agent bundle syntax\n - name: Validate Agent Bundle\n run: sf agent validate authoring-bundle --api-name My_Agent -o TARGET_ORG --json\n\n # Step 3: Publish agent (4-step atomic: validate -> publish -> retrieve -> deploy)\n - name: Publish Agent\n run: sf agent publish authoring-bundle --api-name My_Agent -o TARGET_ORG --json\n\n # Step 4: Activate the published version\n - name: Activate Agent\n run: sf agent activate --api-name My_Agent --version \"$AGENT_VERSION\" -o TARGET_ORG --json\n\n # Optional: Run agent tests after activation\n - name: Run Agent Tests\n run: sf agent test run --api-name MyTestDef --wait 10 -o TARGET_ORG --json\n```\n\nSet `AGENT_VERSION` in the workflow environment to the BotVersion number you intend to activate.\n\n### SF Agent CLI Error Codes (current high-signal set)\n\nUse `sf \u003ccommand> --help` for the full command-specific list. The most relevant current codes are:\n\n| Command | Codes to expect | What they mean |\n|---------|-----------------|----------------|\n| `sf agent validate authoring-bundle` | `0, 1, 2, 3` | `Succeeded`, `Failed` (compilation errors), `NotFound`, `ServerError` |\n| `sf agent publish authoring-bundle` | aligns with validate for compilation failures | publish now uses the same compilation-related codes as validate when Agent Script compilation fails |\n| `sf agent preview start` | `0, 1, 2, 3, 4` | `Succeeded`, `Failed`, `NotFound`, `ServerError`, `PreviewStartFailed` |\n| `sf agent preview send` | `0, 2, 4, 5` | `Succeeded`, `NotFound`, `PreviewSendFailed`, `SessionAmbiguous` |\n| `sf agent preview end` | `0, 2, 4, 5` | `Succeeded`, `NotFound`, `PreviewEndFailed`, `SessionAmbiguous` |\n\n**CI/CD guidance:**\n- block immediately on `1+` for validate / publish\n- if preview returns `SessionAmbiguous (5)`, run `sf agent preview sessions --json`, pick the right session, and retry with `--session-id`\n\n---\n\n## Deployment Pipeline\n\n```\n┌─────────────┐ ┌─────────────┐ ┌─────────────┐\n│ Sandbox │ ───▶ │ Staging │ ───▶ │ Production │\n│ v1.3.0 │ │ Validate │ │ v1.3.0 │\n└─────────────┘ └─────────────┘ └─────────────┘\n```\n\n### 6-Step Pipeline\n\n1. **Retrieve from Sandbox** - Pull latest agent bundle\n2. **Validate Syntax** - Check Agent Script for errors\n3. **Run Tests** - Execute automated agent tests\n4. **Code Review** - Automated best practices checks\n5. **Deploy to Production** - Push validated bundle\n6. **Verify Deployment** - Confirm agent is active\n\n---\n\n## Agent Creation Commands\n\n### Create Agent from Spec File\n\n```bash\n# Generate an agent specification YAML first\nsf agent generate agent-spec \\\n --type customer \\\n --role \"Customer Service Representative\" \\\n --company-name \"Acme Corp\" \\\n --company-description \"E-commerce platform\" \\\n --output-file agent-spec.yaml\n\n# Create the agent from the spec\nsf agent create \\\n --name \"My Service Agent\" \\\n --api-name MyServiceAgent \\\n --spec agent-spec.yaml \\\n -o TARGET_ORG --json\n```\n\n### Managed Packaging Status\n\n`sf agent generate template` is **not** an Agent Script packaging workflow.\nThe current CLI command targets Bot / BotVersion metadata, requires a `.bot-meta.xml` file plus `--source-org`, and doesn't package `.agent` authoring bundles.\n\nFor Agent Script agents today:\n- use source-driven deployment (`sf agent publish authoring-bundle`) for org rollout\n- treat managed-package templating as unsupported for `.agent` bundles\n- see [known-issues.md](known-issues.md) for the current packaging limitation\n\n---\n\n## Programmatic Preview (Non-Interactive)\n\n> ⛔ **NEVER run bare `sf agent preview`** — it launches an interactive terminal requiring keyboard input. Claude Code CANNOT use it. **ALWAYS use subcommands**: `start`, `send`, `end` with `--json`.\n\n```bash\n# 1. Start a published-agent preview session\n# Published agents always use live actions.\nSESSION_ID=$(sf agent preview start --api-name MyAgent -o TARGET_ORG --json | jq -r '.result.sessionId')\n\n# 2. Send utterances programmatically\nsf agent preview send --session-id $SESSION_ID --api-name MyAgent --utterance \"I need a refund\" -o TARGET_ORG --json\n\n# 3. Send follow-up messages\nsf agent preview send --session-id $SESSION_ID --api-name MyAgent --utterance \"Order #12345\" -o TARGET_ORG --json\n\n# 4. End the session\nsf agent preview end --session-id $SESSION_ID --api-name MyAgent -o TARGET_ORG --json\n\n# List cached preview sessions (no target-org flag)\nsf agent preview sessions --json\n```\n\n### Preview Modes\n\n| Target | Required flags | Behavior |\n|--------|----------------|----------|\n| Published agent (`--api-name`) | none | programmatic preview always uses live actions |\n| Authoring bundle (`--authoring-bundle`) | exactly one of `--simulate-actions` or `--use-live-actions` | choose simulated vs real-action execution explicitly |\n\nAdditional preview flags:\n\n| Flag | Purpose |\n|------|---------|\n| `--simulate-actions` | Use AI to simulate action execution. Required with `--authoring-bundle` when you want simulated mode. |\n| `--use-live-actions` | Execute real Apex/Flows/Prompt Templates. Required with `--authoring-bundle` when you want live mode. |\n| `--authoring-bundle` | Preview the local authoring bundle instead of a published Bot. |\n| `--api-name` | Preview an activated published agent. |\n\n```bash\n# Authoring-bundle preview in simulated mode\nSESSION_ID=$(sf agent preview start --authoring-bundle MyBundle --simulate-actions -o TARGET_ORG --json | jq -r '.result.sessionId')\nsf agent preview send --session-id $SESSION_ID --authoring-bundle MyBundle --utterance \"What can you help me with?\" -o TARGET_ORG --json\nsf agent preview end --session-id $SESSION_ID --authoring-bundle MyBundle -o TARGET_ORG --json\n\n# Authoring-bundle preview in live mode\nSESSION_ID=$(sf agent preview start --authoring-bundle MyBundle --use-live-actions -o TARGET_ORG --json | jq -r '.result.sessionId')\n```\n\nIf `preview send` or `preview end` returns `SessionAmbiguous (5)`, run `sf agent preview sessions --json` and retry with the explicit `--session-id`.\n\n---\n\n## Activate / Deactivate Agent\n\nAfter publishing, activate the agent to make it live. Deactivate before re-publishing updates.\n\n```bash\n# Activate agent manually (interactive version selection if omitted)\nsf agent activate --api-name MyAgent -o TARGET_ORG\n\n# Activate a specific BotVersion in CI / automation\nsf agent activate --api-name MyAgent --version \u003cn> -o TARGET_ORG --json\n\n# Deactivate agent (takes it offline — required before re-publishing)\nsf agent deactivate --api-name MyAgent -o TARGET_ORG --json\n```\n\n### Deterministic activation in CI\n\n- `--version \u003cn>` maps to the `vN` suffix of the agent's `BotVersion` metadata.\n- If you omit `--version`, the CLI prompts you to choose a version interactively.\n- If you use `--json` without `--version`, the CLI activates the latest agent version.\n- For reproducible pipelines, pass the exact `--version` you intend to activate.\n\n**Verify activation status**:\n```bash\nsf data query --query \"SELECT DeveloperName, VersionNumber, Status FROM BotVersion WHERE BotDefinition.DeveloperName = 'MyAgent' AND Status = 'Active' LIMIT 1\" -o TARGET_ORG --json\n```\n\n**Full update lifecycle**: Deactivate → Re-publish → Re-activate\n\n```bash\n# Update an already-active agent:\nsf agent deactivate --api-name MyAgent -o TARGET_ORG --json\nsf agent publish authoring-bundle --api-name MyAgent -o TARGET_ORG --json\nsf agent activate --api-name MyAgent --version \u003cn> -o TARGET_ORG --json\n```\n\n---\n\n## Generate Authoring Bundle (`--no-spec`)\n\nThe `--no-spec` flag skips requiring an agent spec YAML file and uses default Agent Script boilerplate:\n\n> **v2.126.4 change**: When `--json` is specified, `--name` is now **required**. The CLI no longer prompts interactively for missing flags in JSON mode — it errors instead. Always include `--name` when scripting with `--json`.\n\n```bash\n# Scaffold a new authoring bundle without an agent spec\nsf agent generate authoring-bundle --no-spec --name \"My Agent\" -o TARGET_ORG --json\n\n# With a spec file (standard flow)\nsf agent generate authoring-bundle --spec agent-spec.yaml --name \"My Agent\" -o TARGET_ORG --json\n\n# Overwrite existing bundle without confirmation (v2.125.1+)\nsf agent generate authoring-bundle --spec agent-spec.yaml --name \"My Agent\" --force-overwrite -o TARGET_ORG --json\n```\n\n---\n\n## Generate Agent Spec — Full Flag Reference\n\n> Optional path only. In this repository, direct `.agent` authoring is the default path; spec generation is a bootstrap aid, not the primary authoring workflow.\n\n| Flag | Values / Type | Description |\n|------|---------------|-------------|\n| `--type` | `customer \\| internal` | **Required.** Agent audience type |\n| `--role` | string | **Required.** Agent's role description |\n| `--company-name` | string | Company name for context |\n| `--company-description` | string | Company description for context |\n| `--company-website` | URL | Company website URL for grounding |\n| `--tone` | `formal \\| casual \\| neutral` | Conversational style |\n| `--enrich-logs` | `true \\| false` | Add agent conversation data to event logs |\n| `--max-topics` | number | Maximum topics to generate (default: 5) |\n| `--agent-user` | username | Einstein Agent User to assign |\n| `--output-file` | path | Output path (default: `specs/agentSpec.yaml`) |\n| `--full-interview` | *(flag)* | Prompt for both required AND optional flags |\n| `--spec` | file path | Use existing spec for iterative refinement |\n| `--prompt-template` | API name | Custom prompt template reference |\n| `--grounding-context` | string | Context value for custom prompt template |\n| `--force-overwrite` | *(flag)* | Overwrite existing spec without confirmation |\n\n```bash\n# Full example with optional flags\nsf agent generate agent-spec \\\n --type customer \\\n --role \"Customer Service Representative\" \\\n --company-name \"Acme Corp\" \\\n --company-description \"E-commerce platform\" \\\n --company-website \"https://acme.example.com\" \\\n --tone formal \\\n --enrich-logs true \\\n --max-topics 8 \\\n --agent-user \"[email protected]\" \\\n --output-file specs/my-agent-spec.yaml\n\n# Iterative refinement — feed existing spec back in\nsf agent generate agent-spec \\\n --spec specs/my-agent-spec.yaml \\\n --max-topics 3 \\\n --tone casual \\\n --force-overwrite\n```\n\n---\n\n## Browser Quick-Open Commands\n\n```bash\n# Open specific agent in Agentforce Builder\nsf org open agent --api-name MyAgent -o TARGET_ORG\n\n# Open Agentforce Studio (list view of all authoring bundles)\nsf org open authoring-bundle -o TARGET_ORG\n\n# Get URL only (for CI/CD logs or scripts)\nsf org open agent --api-name MyAgent -o TARGET_ORG --url-only\n```\n\n---\n\n\u003ca id=\"three-phase-lifecycle\">\u003c/a>\n\n## Three-Phase Lifecycle\n\n```\n┌─────────────┐ ┌─────────────┐ ┌─────────────┐\n│ ✏️ Draft │ → │ 🔒 Commit │ → │ ✅ Activate │\n│ EDITABLE │ │ READ-ONLY │ │ LIVE │\n└─────────────┘ └─────────────┘ └─────────────┘\n```\n\n| Phase | Capabilities |\n|-------|--------------|\n| **Draft** | Edit freely, preview, run batch tests |\n| **Commit** | Script frozen, version assigned, bundle compiled |\n| **Activate** | Assign to Connections, go live, monitor |\n\n> **Key Insight**: Commit doesn't deploy - it freezes. Activate makes it live.\n\n---\n\n## API Versioning Behavior\n\nAgent versions share the same `agentId` (the `BotDefinition` / `Agent` record) but have **distinct version IDs**.\n\n| Concept | Description |\n|---------|-------------|\n| `agentId` / `BotDefinition.Id` | Unique per agent — does NOT change between versions |\n| `versionId` / `BotVersion.Id` | Unique per version — changes with each commit |\n| Default API behavior | API calls target the **active** version unless a specific `versionId` is provided |\n\n```bash\n# The Agent Runtime API defaults to the active version:\ncurl -X POST \".../einstein/ai-agent/v1\" \\\n -d '{\"agentDefinitionId\": \"0XxXXXXXXXXXXX\"}' # Uses active version\n\n# To target a specific version (draft/committed), include versionId:\ncurl -X POST \".../einstein/ai-agent/v1\" \\\n -d '{\"agentDefinitionId\": \"0XxXXXXXXXXXXX\", \"agentVersionId\": \"4KdXXXXXXXXXXX\"}'\n```\n\n> **Key implication**: When testing draft versions via API, you must explicitly pass the version ID. Otherwise, the API will use the last activated version — which may not reflect your latest changes.\n\n---\n\n## ⛔ CLI Anti-Patterns\n\n### DO NOT SOQL query metadata types\n\nThese are **metadata types**, NOT sObjects. SOQL queries against them return `INVALID_TYPE`.\n\n| Type | ❌ WRONG (SOQL) | ✅ CORRECT (Metadata API) |\n|------|-----------------|--------------------------|\n| GenAiPlannerBundle | `SELECT ... FROM GenAiPlannerBundle` | `sf project retrieve start --metadata \"GenAiPlannerBundle:Name\"` |\n| AiAuthoringBundle | `SELECT ... FROM AiAuthoringBundle` | `sf project retrieve start --metadata \"AiAuthoringBundle:Name\"` |\n| GenAiFunction | `SELECT ... FROM GenAiFunction` | `sf project retrieve start --metadata \"GenAiFunction:Name\"` |\n\n> For SOQL, query `BotDefinition` and `BotVersion` instead — these ARE sObjects.\n\n### BotDefinition vs BotVersion field reference\n\n| Field | BotDefinition | BotVersion |\n|-------|:---:|:---:|\n| Id, DeveloperName | ✅ | ✅ |\n| MasterLabel | ✅ | ❌ |\n| **Status** | ❌ | ✅ |\n| VersionNumber | ❌ | ✅ |\n\n> ⚠️ `Status` lives on `BotVersion`, NOT `BotDefinition`. Querying `BotDefinition.Status` returns \"No such column.\"\n\n### DO NOT run bare `sf agent preview`\n\n| ❌ WRONG (interactive, hangs) | ✅ CORRECT (programmatic) |\n|-------------------------------|--------------------------|\n| `sf agent preview` | `sf agent preview start --authoring-bundle MyAgent --simulate-actions -o TARGET_ORG --json` |\n| *(waits for keyboard input)* | `sf agent preview send --session-id $ID --authoring-bundle MyAgent --utterance \"...\" -o TARGET_ORG --json` |\n| | `sf agent preview end --session-id $ID --authoring-bundle MyAgent -o TARGET_ORG --json` |\n\n### DO NOT pass context variables via `sf agent preview`\n\n> ⛔ `sf agent preview` does NOT support context or session variable injection. There are no `--context`, `--session-var`, or `--variables` flags.\n\n| Variable Source | Works in Preview? | Alternative |\n|----------------|:-----------------:|-------------|\n| `@session.sessionID` | ❌ | Agent Runtime API with session context |\n| `@context.customerId` | ❌ | Agent Runtime API with `contextVariables` |\n| `@context.RoutableId` | ❌ | Agent Runtime API with `contextVariables` |\n| Mutable vars (defaults) | ✅ | Works normally via default values |\n| `with param=...` (slot-filling) | ✅ | Works normally via LLM extraction |\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":30615,"content_sha256":"27914d31a382c1c256da2db16bdfc48c602c8542baaae7296f162dac375f1706"},{"filename":"references/complex-data-types.md","content":"\u003c!-- Parent: sf-ai-agentscript/SKILL.md -->\n\n# `complex_data_type_name` Mapping Table\n\n> **\"#1 source of compile errors\"** - Use this table when defining action inputs/outputs in Agentforce Assets.\n\n| Data Type | `complex_data_type_name` Value | Notes |\n|-----------|-------------------------------|-------|\n| `string` | *(none needed)* | Primitive type |\n| `number` | *(none needed)* | Primitive type — **variables only**† |\n| `boolean` | *(none needed)* | Primitive type |\n| `object` (SObject) | `lightning__recordInfoType` | Use for Account, Contact, etc. |\n| `list[string]` | `lightning__textType` | Collection of text values |\n| `list[object]` (JSON text) | `lightning__textType` | Serialized as JSON text |\n| `list[object]` (SObject collection) | `lightning__recordInfoType` | **USE THIS for Flow SObject outputs** |\n| Apex Inner Class | `@apexClassType/NamespacedClass__InnerClass` | Namespace required |\n| Custom LWC Type | `lightning__c__CustomTypeName` | Custom component types |\n| Currency field | `lightning__currencyType` | For monetary values |\n| `datetime` | `lightning__dateTimeStringType` | DateTime values (TDD v2.1.0) |\n| `integer` | `lightning__integerType` | Integer numbers (action I/O only) |\n| `double` / `number` | `lightning__doubleType` | Decimal/floating-point numbers (action I/O only) |\n| `object` (structured) | `lightning__objectType` | Complex data structures (action I/O only) |\n| `list` (generic) | `lightning__listType` | Arrays/lists (action I/O only) |\n\n> **† WARNING: #1 Publish Error Source** — Bare `number` works in `variables:` declarations\n> but **FAILS at publish** in action `inputs:`/`outputs:`. For action I/O, you MUST use:\n> - Integer values → `lightning__integerType`\n> - Decimal values → `lightning__doubleType`\n\n> **Naming variance**: Upstream documentation uses `lightning__dateTimeType` while our TDD-validated testing confirms `lightning__dateTimeStringType`. Both may be valid depending on API version — use `lightning__dateTimeStringType` as the tested default.\n\n## Agent Script → Lightning Type Mapping (TDD Validated v2.1.0)\n\n> Use this when troubleshooting type errors between Agent Script action I/O and Apex/Flow targets.\n\n| Agent Script Type | Lightning Type | Apex Type | Notes |\n|-------------------|---------------|-----------|-------|\n| `string` | `lightning__textType` | `String` | No `complex_data_type_name` needed |\n| `number` | `lightning__numberType` | `Decimal` / `Double` | No `complex_data_type_name` needed |\n| `boolean` | `lightning__booleanType` | `Boolean` | No `complex_data_type_name` needed |\n| `datetime` | `lightning__dateTimeStringType` | `DateTime` | **Actions only** — not valid for variables |\n| `date` | `lightning__dateType` | `Date` | Valid for both variables and actions |\n| `currency` | `lightning__currencyType` | `Decimal` | Annotated with currency type |\n\n## Flow Type -> Agent Script Mapping (Production-Validated)\n\n> When defining action I/O for `flow://` targets, use this table to map Flow variable types to Agent Script declarations.\n\n| Flow Type | Agent Script Type | `complex_data_type_name` | Notes |\n|-----------|------------------|--------------------------|-------|\n| `String` | `string` | *(none needed)* | Or `lightning__textType` (optional) |\n| `Date` | `object` | `lightning__dateType` | Must use `object`, not `date` |\n| `DateTime` | `object` | `lightning__dateTimeStringType` | NOT `lightning__dateTimeType` |\n| `Boolean` | `boolean` | *(none needed)* | Primitive type |\n| `SObject` collection | `list[object]` | `lightning__recordInfoType` | NOT `lightning__objectType` |\n\n> **Common Mistake**: Using `lightning__textType` for SObject collection outputs. The publisher accepts it but the LLM receives serialized JSON text instead of structured records, breaking field access patterns.\n\n**Pro Tip**: Don't manually edit `complex_data_type_name` - use the UI dropdown in **Agentforce Assets > Action Definition**, then export/import the action definition.\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":3988,"content_sha256":"ceb1c23a6d0aca40ef283ee3c60a87eeb79de189aab837a6b452654c221e5f2c"},{"filename":"references/customer-web-client.md","content":"\u003c!-- Parent: sf-ai-agentscript/SKILL.md -->\n\n# Phase 5.5: CustomerWebClient Surface Enablement\n\n> ⚠️ **Without `CustomerWebClient` surface, Agent Builder Preview shows \"Something went wrong\" and Agent Runtime API / CLI testing returns `500 Internal Server Error`.**\n\n**The Problem**: `connection messaging:` in the `.agent` file ONLY generates a `Messaging` plannerSurface during compilation. There is NO `connection customerwebclient:` DSL syntax — attempting it causes `ERROR_HTTP_404` on publish. Agent Builder Preview + CLI testing require `CustomerWebClient`, which must be manually patched after every publish.\n\n## Post-Publish Patch Workflow (6 steps, required after EVERY publish)\n\n```bash\n# Step 1: Publish the agent (creates new version, e.g., v22)\nsf agent publish authoring-bundle --api-name AgentName -o TARGET_ORG --json\n\n# Step 2: Retrieve the compiled GenAiPlannerBundle\nsf project retrieve start --metadata \"GenAiPlannerBundle:AgentName_vNN\" -o TARGET_ORG --json\n\n# Step 3: Add CustomerWebClient plannerSurface to the XML (see template below)\n\n# Step 4: Deactivate agent (deploy fails while active)\nsf agent deactivate --api-name AgentName -o TARGET_ORG --json\n\n# Step 5: Deploy patched bundle\nsf project deploy start --metadata \"GenAiPlannerBundle:AgentName_vNN\" -o TARGET_ORG --json\n\n# Step 6: Reactivate the same BotVersion created in Step 1\nsf agent activate --api-name AgentName --version NN -o TARGET_ORG --json\n```\n\n> Use the same `NN` value from the `GenAiPlannerBundle:AgentName_vNN` version you just patched so the reactivation is deterministic.\n\n## Step 3 — XML patch template\n\nAdd after existing `Messaging` plannerSurfaces block:\n```xml\n\u003c!-- Add this SECOND plannerSurfaces block for CustomerWebClient -->\n\u003cplannerSurfaces>\n \u003cadaptiveResponseAllowed>false\u003c/adaptiveResponseAllowed>\n \u003ccallRecordingAllowed>false\u003c/callRecordingAllowed>\n \u003coutboundRouteConfigs>\n \u003cescalationMessage>Your escalation message here.\u003c/escalationMessage>\n \u003coutboundRouteName>Your_OmniChannel_Flow_Name\u003c/outboundRouteName>\n \u003coutboundRouteType>OmniChannelFlow\u003c/outboundRouteType>\n \u003c/outboundRouteConfigs>\n \u003csurface>SurfaceAction__CustomerWebClient\u003c/surface>\n \u003csurfaceType>CustomerWebClient\u003c/surfaceType>\n\u003c/plannerSurfaces>\n```\n\n> **Note**: The `outboundRouteConfigs` should mirror your Messaging surface config. If no escalation routing is configured, omit the `outboundRouteConfigs` block.\n\n> **Note**: `EinsteinAgentApiChannel` surfaceType is NOT available on all orgs (see Known Issue 17). Use `CustomerWebClient` instead — it enables both Agent Builder Preview and Agent Runtime API access.\n\n## ECA for Client Credentials Flow\n\nThe Agent Runtime API requires OAuth with `chatbot_api`, `sfap_api`, and `api` scopes. See `/sf-connected-apps` for ECA creation. If you only need interactive testing via `sf agent preview`, skip ECA setup — preview uses standard org auth (`sf org login web`, v2.121.7+).\n\n## Validation\n\n```bash\n# Acquire token\ncurl -X POST \"https://YOUR_DOMAIN.my.salesforce.com/services/oauth2/token\" \\\n -d \"grant_type=client_credentials&client_id=KEY&client_secret=SECRET\"\n\n# Create agent session\ncurl -X POST \"https://YOUR_DOMAIN.my.salesforce.com/einstein/ai-agent/v1\" \\\n -H \"Authorization: Bearer TOKEN\" \\\n -H \"Content-Type: application/json\" \\\n -d '{\"agentDefinitionId\":\"0XxXXXXXXXXXXXXXXX\"}'\n```\n\n> **Symptom**: 500 error with `\"errorCode\": \"UNKNOWN_EXCEPTION\"` → Missing `CustomerWebClient` plannerSurface.\n> **Symptom**: Agent Builder Preview → \"Something went wrong. Refresh and try again.\" → Same root cause.\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":3599,"content_sha256":"b8c1546f9c1bbbeeea90f95d1fe7dbcfcda4bc57203f911146102470b11d02b7"},{"filename":"references/debugging-guide.md","content":"\u003c!-- Parent: sf-ai-agentscript/SKILL.md -->\n# Debugging & Observability Guide\n\n> Find the Leak Before It Finds You\n\n---\n\n## The Debugging Workflow\n\n```\n┌─────────────────────┐ ┌─────────────────────┐ ┌─────────────────────┐\n│ 📋 Interaction │ → │ 📊 Trace │ → │ ⚙️ Find the │\n│ Details │ │ Waterfall │ │ Leak │\n└─────────────────────┘ └─────────────────────┘ └─────────────────────┘\n```\n\n---\n\n## The 4 Debugging Views\n\n| Tab | Icon | Description |\n|-----|------|-------------|\n| 📋 Interaction Details | List | The Summary View |\n| 📊 Trace Waterfall | Chart | The Technical View |\n| ↔️ Variable State Tracking | Arrows | Entry vs. Exit Values |\n| \u003c> Script View with Linting | Code | Red Squiggles |\n\n---\n\n### View 1: Interaction Details (Summary)\n\n**Purpose**: High-level chronological list with AI-generated summaries\n\n**Shows:**\n- ✅ Input received from user\n- ✅ Reasoning decisions made\n- ✅ Actions executed\n- ✅ Output evaluation results\n\n**Best For**: Quickly understanding WHAT happened\n\n---\n\n### View 2: Trace Waterfall (Technical)\n\n**Purpose**: Granular view showing every internal step\n\n**Shows:**\n- ✅ Exact prompt sent to the LLM\n- ✅ Latency for each span (milliseconds)\n- ✅ Raw JSON input/output for every tool call\n- ✅ Variable state at each step\n\n**Best For**: Understanding WHY something happened\n\n---\n\n### View 3: Variable State Tracking\n\n**Purpose**: Real-time table of variable Entry vs Exit values\n\n**Shows:**\n- ✅ Which variables changed during each span\n- ✅ Critical security variables (verified, customer_id)\n- ✅ Values LLM used vs values it should have used\n\n**Best For**: Finding when LLM ignored variable state (\"goal drift\")\n\n---\n\n### View 4: Script View with Linting\n\n**Purpose**: Agent Script code with real-time syntax validation\n\n**Shows:**\n- ✅ Missing required top-level blocks\n- ✅ Indentation issues\n- ✅ Missing required fields\n- ✅ Invalid resource references\n\n**Best For**: Catching errors before deployment\n\n---\n\n### View Selection Guide\n\n| Question | Use This View |\n|----------|---------------|\n| \"What happened in this conversation?\" | **Interaction Details** |\n| \"What exactly did the LLM see?\" | **Trace Waterfall** |\n| \"Why did the variable have wrong value?\" | **Variable State** |\n| \"Why won't my agent compile?\" | **Script View** |\n\n---\n\n## The 6 Span Types\n\n| # | Span Type | Internal Name | Description |\n|---|-----------|---------------|-------------|\n| 1 | ➡️ **Topic Enter** | `topic_enter` | Execution enters a new topic |\n| 2 | ▶ **before_reasoning** | `before_reasoning` | Deterministic pre-processing |\n| 3 | 🧠 **reasoning** | `reasoning` | LLM processes instructions |\n| 4 | ⚡ **Action Call** | `action_call` | Action invoked |\n| 5 | → **Transition** | `transition` | Topic navigation |\n| 6 | ✓ **after_reasoning** | `after_reasoning` | Deterministic post-processing |\n\n---\n\n## Reading a Trace Waterfall\n\n### Example Timeline\n\n```\nSPAN DURATION TIMELINE\n────────────────────────────────────────────────────────────────────\n➡️ Topic Enter 15ms ████░░░░░░░░░░░░░░░░░░░░░░░░░░░░\n▶ before_reasoning 850ms ████████████████████░░░░░░░░░░░░\n🧠 reasoning 1250ms ██████████████████████████████░░\n⚡ Action Call 450ms ██████████████░░░░░░░░░░░░░░░░░░\n```\n\n### Latency Benchmarks\n\n| Span Type | Expected Duration | If Slower... |\n|-----------|-------------------|--------------|\n| `topic_enter` | 10-20ms | Check topic complexity |\n| `before_reasoning` | 50-500ms | Data fetch issues |\n| `reasoning` | 1-3s | Normal LLM latency |\n| `action_call` | 100-500ms | External service slow |\n| `after_reasoning` | 10-50ms | Logging overhead |\n\n---\n\n## Variable State Analysis\n\n### Entry vs Exit Pattern\n\n| Step | Variable | Entry | Exit | Problem? |\n|------|----------|-------|------|----------|\n| 1 | `customer_verified` | `False` | `False` | - |\n| 2 | `customer_verified` | `False` | `True` | - |\n| 3 | `refund_processed` | `False` | `True` | ⚠️ Processed while verified=False! |\n\n> **KEY INSIGHT**: If a critical variable like `is_verified` was `False` when an action executed, you've found your leak point.\n\n---\n\n## Common Debug Patterns\n\n### Pattern 1: Wrong Policy Applied\n\n**Symptom**: Customer received wrong regional policy\n\n**Trace Analysis:**\n1. Check Variable State → `CustomerCountry` was empty at filter step\n2. Check variable declaration → `mutable string = \"\"`\n3. **Root Cause**: Should be `linked string` with `source: @session.Country`\n\n**Fix:**\n```yaml\n# Wrong\nCustomerCountry: mutable string = \"\"\n\n# Correct\nCustomerCountry: linked string\n source: @session.Country\n```\n\n---\n\n### Pattern 2: Action Executed Without Authorization\n\n**Symptom**: Refund processed without identity verification\n\n**Trace Analysis:**\n1. Check reasoning span → LLM selected `process_refund`\n2. Check action definition → No `available when` guard\n3. **Root Cause**: LLM could see and select unguarded action\n\n**Fix:**\n```yaml\n# Wrong - no guard\nprocess_refund: @actions.process_refund\n description: \"Issue refund\"\n\n# Correct - guarded\nprocess_refund: @actions.process_refund\n description: \"Issue refund\"\n available when @variables.customer_verified == True\n```\n\n---\n\n### Pattern 3: Post-Action Logic Didn't Run\n\n**Symptom**: CRM case wasn't created after refund approval\n\n**Trace Analysis:**\n1. Check instruction resolution order → Post-action check at bottom\n2. Check transition → Topic transitioned before check could run\n3. **Root Cause**: Post-action check must be at TOP\n\n**Fix:**\n```yaml\n# Wrong - check at bottom\ninstructions: ->\n | Help with refund.\n transition to @topic.next\n\n if @variables.refund_done: # Never reaches here!\n run @actions.log_refund\n\n# Correct - check at TOP\ninstructions: ->\n if @variables.refund_done:\n run @actions.log_refund\n transition to @topic.success\n\n | Help with refund.\n```\n\n---\n\n### Pattern 4: Infinite Loop\n\n**Symptom**: Agent keeps returning to same topic\n\n**Trace Analysis:**\n1. Check transitions → `topic_enter` repeating for same topic\n2. Check conditions → No exit condition defined\n3. **Root Cause**: Missing state change or exit condition\n\n**Fix:**\n```yaml\n# Wrong - no exit condition\ninstructions: ->\n | Continue processing.\n\n# Correct - track state, add exit\ninstructions: ->\n if @variables.processing_complete == True:\n transition to @topic.done\n\n | Continue processing.\n set @variables.step_count = @variables.step_count + 1\n```\n\n---\n\n### Pattern 5: LLM Ignores Variable State (Goal Drift)\n\n**Symptom**: LLM makes decision contradicting variable value\n\n**Trace Analysis:**\n1. Check Variable State → Variable had correct value\n2. Check resolved instructions → Condition should have pruned text\n3. **Root Cause**: Using pipe syntax (`|`) instead of arrow (`->`)\n\n**Fix:**\n```yaml\n# Wrong - pipe doesn't support conditionals\ninstructions: |\n if @variables.verified:\n Help the user.\n # This is literal text, not a condition!\n\n# Correct - arrow enables conditionals\ninstructions: ->\n if @variables.verified:\n | Help the user.\n else:\n | Please verify first.\n```\n\n---\n\n## Diagnostic Checklist\n\n### Quick Triage\n\n| Check | Command/Action |\n|-------|----------------|\n| Syntax valid? | `sf agent validate authoring-bundle --api-name MyAgent -o TARGET_ORG --json` |\n| User exists? | `sf data query -q \"SELECT Username FROM User WHERE Profile.Name='Einstein Agent User'\"` |\n| Topic exists? | Search for topic name in script |\n| Variable initialized? | Check `variables:` block |\n\n### Deep Investigation\n\n| Issue | What to Check |\n|-------|---------------|\n| Wrong output | Variable State (Entry/Exit values) |\n| Skipped logic | Instruction resolution order |\n| Security bypass | `available when` guards |\n| Data missing | Action target protocol, linked variable sources |\n| Slow response | Trace Waterfall latencies |\n\n---\n\n## The Big Picture\n\n> **\"Prompts are suggestions. Guards are guarantees.\"**\n\nThe LLM might ignore your instructions. The only way to truly prevent unwanted behavior is through **deterministic guards** like `available when`.\n\n**When you remove an action from the LLM's toolkit, it literally cannot invoke it. That's not a suggestion - that's enforcement.**\n\n---\n\n## Key Takeaways\n\n| # | Takeaway |\n|---|----------|\n| 1 | **Two Views for Two Purposes** - Interaction Details for quick understanding, Trace Waterfall for forensics |\n| 2 | **Entry vs Exit Reveals Problems** - Variable state changes show exactly when/where issues occurred |\n| 3 | **`available when` Blocks Actions** - Makes unauthorized actions invisible, not just discouraged |\n| 4 | **Post-Action at TOP** - Check for completed actions at the start of instructions |\n| 5 | **Linked vs Mutable** - Wrong variable modifier causes empty values |\n\n---\n\n## Programmatic Trace Access via CLI\n\n> `sf agent preview start/send/end` provides full v1.1 trace data with 13 step types — far richer than the 5-type STDM data in Builder UI.\n\n### Workflow\n\n```bash\n# 1. Start preview session\nSESSION_ID=$(sf agent preview start \\\n --api-name My_Agent \\\n --target-org myOrg --json 2>/dev/null | jq -r '.result.sessionId')\n\n# 2. Send utterance(s)\nPLAN_ID=$(sf agent preview send \\\n --session-id \"$SESSION_ID\" \\\n --api-name My_Agent \\\n --utterance \"I need help with my order\" \\\n --target-org myOrg --json 2>/dev/null | jq -r '.result.messages[-1].planId')\n\n# 3. End session and get trace path\nTRACES_PATH=$(sf agent preview end \\\n --session-id \"$SESSION_ID\" \\\n --api-name My_Agent \\\n --target-org myOrg --json 2>/dev/null | jq -r '.result.tracesPath')\n\n# 4. Analyze trace\njq '.' \"$TRACES_PATH/$PLAN_ID.json\"\n```\n\n### Key `jq` Recipes for Agent Script Debugging\n\n```bash\nTRACE=\"$TRACES_PATH/$PLAN_ID.json\"\n\n# What instructions did the LLM actually receive?\njq -r '.steps[] | select(.stepType == \"LLMStep\") | .data.prompt_content[0].content' \"$TRACE\"\n\n# Which topic was selected and why?\njq '.steps[] | select(.stepType == \"TransitionStep\") | {from: .data.from, to: .data.to}' \"$TRACE\"\n\n# What actions were available per iteration?\njq '.steps[] | select(.stepType == \"EnabledToolsStep\") | .data.enabled_tools' \"$TRACE\"\n\n# Did any action fail?\njq '.steps[] | select(.stepType == \"FunctionStep\") | {fn: .function, error: .error}' \"$TRACE\"\n\n# Variable state changes (verify instruction resolution)\njq '.steps[] | select(.stepType == \"VariableUpdateStep\") | {var: .data.variableName, old: .data.oldValue, new: .data.newValue}' \"$TRACE\"\n```\n\n### Trace ↔ Agent Script Mapping\n\n| Agent Script Concept | Trace Step Type | What to Check |\n|---------------------|-----------------|---------------|\n| `instructions: ->` | `LLMStep.prompt_content[0]` | Verify resolved text appears |\n| `when` blocks | `LLMStep.prompt_content[3]` | Verify late-injected context |\n| `actions:` definitions | `EnabledToolsStep.enabled_tools` | Verify action visibility |\n| `available when:` | `EnabledToolsStep` (per iteration) | Verify conditional filtering |\n| `transition to` | `TransitionStep` | Verify from→to routing |\n| `run @actions.X` | `FunctionStep` | Verify inline action execution |\n| Variable assignments | `VariableUpdateStep` | Verify state mutations |\n\n> For full trace schema and analysis patterns, see **sf-ai-agentforce-testing** [trace-analysis.md](../../sf-ai-agentforce-testing/references/trace-analysis.md).\n\n---\n\n## Planner Engine Note\n\nAgent Script compiles against the Atlas planner engine. Legacy Java planner behavior is not relevant for Agent Script agents. If you encounter \"it worked in dev but not in prod\" issues, focus on variable state, action I/O, and permission differences rather than planner engine variations.\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":12477,"content_sha256":"11ea7f82ca69ab3bcb887f350cec744d7f6b9b399e81b1495ab7f5b519d5042f"},{"filename":"references/feature-validity.md","content":"\u003c!-- Parent: sf-ai-agentscript/SKILL.md -->\n\n# Feature Validity by Context (TDD Validated v2.2.0)\n\n> **Key distinction**: Many action metadata properties are valid on **action definitions with targets** (`flow://`, `apex://`) but NOT on **utility actions** (`@utils.transition`). The v1.3.0 finding that tested only `@utils.transition` was too narrow — v2.2.0 corrects this.\n\n| Feature | On `@utils.transition` | On action definitions with `target:` | Notes |\n|---------|------------------------|---------------------------------------|-------|\n| `label:` on topics | ❌ v1.3.0 | ✅ v2.2.0 | Valid on topic blocks |\n| `label:` on actions | ❌ v1.3.0 | ✅ v2.2.0 | Valid on Level 1 action definitions |\n| `label:` on I/O fields | ❌ v1.3.0 | ✅ v2.2.0 | Valid on inputs/outputs |\n| `require_user_confirmation:` | ❌ | ✅ v2.2.0 | Compiles; runtime no-op (Issue 6) |\n| `include_in_progress_indicator:` | ❌ | ✅ v2.2.0 | Shows spinner during action execution |\n| `progress_indicator_message:` | ❌ | ✅ v2.2.0 | Works on both `flow://` and `apex://` |\n| `output_instructions:` | ❌ | ❓ Untested | Not tested on target-backed actions |\n| `always_expect_input:` | ❌ | ❌ | NOT implemented anywhere |\n\n**What works on `@utils.transition` actions:**\n```yaml\nactions:\n go_next: @utils.transition to @topic.next\n description: \"Navigate to next topic\" # ✅ ONLY description works\n```\n\n**What works on action definitions with `target:`:**\n```yaml\nactions:\n process_order:\n label: \"Process Order\" # ✅ Display label\n description: \"Process the customer's order\" # ✅ LLM description\n require_user_confirmation: True # ✅ Compiles (runtime Issue 6)\n include_in_progress_indicator: True # ✅ Shows spinner\n progress_indicator_message: \"Processing...\" # ✅ Custom spinner message\n inputs:\n order_id: string\n label: \"Order ID\" # ✅ I/O display label\n description: \"The order identifier\"\n outputs:\n status: string\n label: \"Order Status\" # ✅ I/O display label\n description: \"Current order status\"\n target: \"apex://OrderProcessor\"\n```\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":2275,"content_sha256":"b7bc04aa5af268114114c77c449e9ccfa388223f1edfd10237345c21d5cdd94f"},{"filename":"references/fsm-architecture.md","content":"\u003c!-- Parent: sf-ai-agentscript/SKILL.md -->\n# FSM Architecture Guide\n\n> Design agent behavior as a finite state machine with deterministic nodes and explicit transitions.\n\n---\n\n## Why FSM Architecture?\n\n### The Problem with Prompt-Only Agents\n\n| Anti-Pattern | Description |\n|--------------|-------------|\n| **ReAct Pattern** | Agents get stuck in reasoning loops without guardrails |\n| **Doom-Prompting** | Prompts grow exponentially to handle edge cases |\n| **Goal Drift** | Agents forget original intent after several turns |\n\n> **KEY INSIGHT**: \"LLMs are non-deterministic by design. Without structured control flow, enterprise agents become unpredictable, expensive, and impossible to debug.\"\n\n### The FSM Solution\n\n| FSM Concept | Traffic Light Example | Agent Benefit |\n|-------------|----------------------|---------------|\n| **States** | Red, Green, Yellow | Agent always knows exactly what it's doing |\n| **Transitions** | Timer expires | No ambiguity about what happens next |\n| **Determinism** | Red → Green (guaranteed) | Auditable, testable, trustworthy |\n\n---\n\n## The Three FSM Pillars\n\n| Pillar | Definition | Agent Benefit |\n|--------|------------|---------------|\n| **States** | Distinct \"modes\" the system can be in | Clear context at any moment |\n| **Transitions** | Explicit rules for moving between states | Defined paths, no surprises |\n| **Determinism** | Same input → same output | Auditable and testable |\n\n---\n\n## The 5 Node Patterns\n\n### Pattern Overview\n\n| Pattern | Color | Purpose |\n|---------|-------|---------|\n| 🔵 **ROUTING** | Blue | Routes based on intent |\n| 🔵 **VERIFICATION** | Light Blue | Security checks |\n| 🟡 **DATA-LOOKUP** | Yellow | External data fetch |\n| 🟢 **PROCESSING** | Green | Business logic |\n| 🔴 **HANDOFF** | Red | Human escalation |\n\n---\n\n### Pattern 1: ROUTING (Topic Selector)\n\n**Purpose**: Routes conversations based on detected intent\n\n```yaml\nstart_agent topic_selector:\n description: \"Route to appropriate topic based on intent\"\n reasoning:\n instructions: ->\n | You are the support agent.\n Classify the customer's intent and route:\n - Refund requests go to identity verification\n - General inquiries are handled directly\n actions:\n start_refund: @utils.transition to @topic.identity_verification\n description: \"Customer wants a refund\"\n handle_inquiry: @utils.transition to @topic.general_support\n description: \"General question or inquiry\"\n```\n\n**When to Use**: Entry point for multi-purpose agents\n\n#### Routing Heuristics\n\n> **Terminology note:** Public Agentforce docs increasingly say **subagent**. In Agent Script and this repo, the routing unit is still a **topic**.\n\n- Use `description:` as the primary routing signal. Use `label:` for display only.\n- Topic names should be short and literal; descriptions should capture the exact job to be done.\n- If one utterance fits two topics, narrow, merge, or rename them.\n- Keep escalation, handoff, and frustration separate from business-domain intents. Broad escalation descriptions absorb real work.\n- Expect re-classification on every new turn. Protected actions and gates must still hold after rerouting.\n\n---\n\n### Pattern 2: VERIFICATION (Identity Gate)\n\n**Purpose**: Enforces security checks before proceeding\n\n```yaml\ntopic identity_verification:\n description: \"Verify customer identity before refund\"\n reasoning:\n instructions: ->\n if @variables.failed_attempts >= 3:\n | Too many failed attempts. Escalating to human agent.\n if @variables.email_verified == True:\n | Identity verified. Proceed to risk assessment.\n else:\n | Ask customer to verify their email address.\n actions:\n verify_email: @actions.verify_email\n description: \"Verify customer email\"\n with email = @variables.customer_email\n set @variables.email_verified = @outputs.verified\n proceed: @utils.transition to @topic.risk_assessment\n description: \"Continue to risk assessment\"\n available when @variables.email_verified == True\n escalate: @utils.escalate\n description: \"Transfer to human agent\"\n available when @variables.failed_attempts >= 3\n```\n\n**When to Use**: Before accessing sensitive data or actions\n\n---\n\n### Pattern 3: DATA-LOOKUP (Risk Assessment)\n\n**Purpose**: Fetches data from external sources\n\n```yaml\ntopic risk_assessment:\n description: \"Fetch customer data and assess churn risk\"\n reasoning:\n instructions: ->\n | Fetch customer profile using the action.\n Once loaded, review:\n - Churn Risk: {[email protected]_risk_score}%\n - Lifetime Value: {[email protected]_value}\n actions:\n get_profile: @actions.get_customer_profile\n description: \"Load customer data from CRM\"\n with customer_id = @variables.customer_id\n set @variables.churn_risk_score = @outputs.churn_risk\n set @variables.lifetime_value = @outputs.ltv\n process_refund: @utils.transition to @topic.refund_processor\n description: \"Continue to refund processing\"\n```\n\n**When to Use**: When decisions require external data\n\n---\n\n### Pattern 4: PROCESSING (Refund Processor)\n\n**Purpose**: Applies business logic based on conditions\n\n```yaml\ntopic refund_processor:\n description: \"Process refund based on churn risk\"\n reasoning:\n instructions: ->\n if @variables.churn_risk_score >= 80:\n | HIGH CHURN RISK - Approve full refund.\n if @variables.churn_risk_score \u003c 80:\n | LOW CHURN RISK - Offer partial credit.\n actions:\n approve_full: @actions.process_refund\n available when @variables.churn_risk_score >= 80\n with amount = @variables.order_total\n with type = \"full\"\n offer_credit: @actions.issue_credit\n available when @variables.churn_risk_score \u003c 80\n with amount = 10\n```\n\n**When to Use**: Applying business rules to data\n\n---\n\n### Pattern 5: HANDOFF (Escalation)\n\n**Purpose**: Transfers conversation to human agent\n\n```yaml\ntopic escalation:\n description: \"Escalate to human agent\"\n reasoning:\n instructions: ->\n | Customer has failed verification 3 times.\n Escalating to a human agent for assistance.\n actions:\n handoff: @utils.escalate\n description: \"Transfer to human agent\"\n```\n\n**When to Use**: Failed verification, complex issues, customer request\n\n#### Escalation with Availability Check\n\n> **Production-validated issue**: When `@utils.escalate` is invoked but no human agents are available, the conversation re-enters the topic and triggers the escalation logic again — creating an infinite loop. The built-in 3-4 loop guardrail eventually breaks the cycle, but the user experience is poor (repeated \"Connecting you...\" messages).\n\n**Fix**: Use a latch variable to detect re-entry after a failed escalation, and route to a fallback topic.\n\n```yaml\nvariables:\n escalation_attempted: mutable boolean = False\n\ntopic escalation:\n description: \"Escalate to human agent\"\n\n before_reasoning:\n if @variables.escalation_attempted == True:\n # Already attempted — agents unavailable, go to fallback\n transition to @topic.leave_message\n\n set @variables.escalation_attempted = True\n\n reasoning:\n instructions: ->\n | I'm connecting you with a support specialist now.\n actions:\n handoff: @utils.escalate\n description: \"Transfer to human agent\"\n\ntopic leave_message:\n description: \"Collect contact info when agents are unavailable\"\n reasoning:\n instructions: |\n Our team is currently unavailable. Please leave your name\n and a brief description of your issue, and we'll follow up.\n```\n\n**Key Points:**\n- `@utils.escalate` does NOT return a success/failure status — you cannot check if escalation succeeded\n- Use `before_reasoning:` (FREE, deterministic) to detect re-entry before the LLM runs\n- Always provide a fallback topic (leave message, callback request, self-service) for graceful degradation\n- See [known-issues.md](known-issues.md#issue-31) Issue 31 for full details\n\n---\n\n## Architecture Patterns\n\n### Pattern 1: Hub and Spoke\n\nCentral router to specialized topics.\n\n```\n ┌─────────────┐\n │ topic_sel │\n │ (hub) │\n └──────┬──────┘\n ┌─────────┼─────────┐\n ▼ ▼ ▼\n┌────────┐ ┌────────┐ ┌────────┐\n│refunds │ │ orders │ │support │\n│(spoke) │ │(spoke) │ │(spoke) │\n└────────┘ └────────┘ └────────┘\n```\n\n**When to Use**: Multi-purpose agents with distinct request types\n\n---\n\n### Pattern 2: Linear Flow\n\nSequential A → B → C pipeline.\n\n```\n┌───────┐ ┌────────┐ ┌─────────┐ ┌─────────┐\n│ entry │ → │ verify │ → │ process │ → │ confirm │\n└───────┘ └────────┘ └─────────┘ └─────────┘\n```\n\n**When to Use**: Mandatory steps (onboarding, checkout, compliance)\n\n---\n\n### Pattern 3: Escalation Chain\n\nTiered support with complexity-based routing.\n\n```\n┌───────┐ ┌───────┐ ┌──────────┐ ┌─────────┐\n│ L1 │ → │ L2 │ → │ L3 │ → │ human │\n│(basic)│ │ (adv) │ │ (expert) │ │ agent │\n└───┬───┘ └───┬───┘ └────┬─────┘ └─────────┘\n ▼ ▼ ▼\n[resolved] [resolved] [resolved]\n```\n\n**When to Use**: Support workflows with complexity levels\n\n---\n\n### Pattern 4: Verification Gate\n\nSecurity gate before protected topics.\n\n```\n ┌───────────┐\n │ entry │\n └─────┬─────┘\n ▼\n ┌───────────┐◄─────────────┐\n │ VERIFY │ │\n │ (GATE) │ │\n └─────┬─────┘ │\n ┌─────────┴─────────┐ │\n ▼ ▼ │\n [verified=True] [verified=False]──┘\n │\n ┌─────┴─────────────┐\n ▼ ▼ ▼\n┌─────────┐┌─────────┐┌──────────┐\n│ account ││payments ││ settings │\n│(protect)││(protect)││(protected)│\n└─────────┘└─────────┘└──────────┘\n```\n\n**When to Use**: Sensitive data, payments, PII access\n\n#### Auth Gate Leak Anti-Pattern\n\n> **Production-validated issue**: Escalation topics with broad descriptions absorb auth-gated intents, bypassing the verification gate entirely.\n\n**The Problem**: When an Escalation topic's description includes broad terms like \"billing\", \"payments\", or \"appointment changes\", the topic classifier routes those intents directly to Escalation — skipping the Verification Gate.\n\n**Example**: User says \"Make a payment\" -> Expected: Verification Gate -> Payments topic. Actual: Escalation topic (because its description mentions \"billing inquiries\").\n\n**Common leak intents** (confirmed in production):\n- \"update credit card\" -> Escalation (instead of Auth -> Payment Settings)\n- \"reschedule appointment\" -> Escalation (instead of Auth -> Appointments)\n- \"pay my bill\" -> Escalation (instead of Auth -> Payments)\n- \"when will my package arrive\" -> Escalation (instead of Auth -> Shipping)\n- \"billing issue\" -> Escalation (instead of Auth -> Payments)\n\n**Fix**:\n1. **Narrow the Escalation description** — Remove business-domain terms. Keep it focused on meta-intents: \"Customer explicitly requests a human agent\" or \"Customer is frustrated and wants to speak to someone\"\n2. **Guard protected topics with `available when`** — Add `available when @variables.verified == True` to all post-auth actions so even if routing leaks, the actions are invisible\n3. **Test for leaks** — For every auth-gated intent, write a test case asserting the expected auth-first routing path\n\n```yaml\n# ❌ Broad escalation description — absorbs payment/appointment intents\ntopic escalation:\n description: \"Handles billing inquiries, appointment issues, account problems, and customer frustration\"\n\n# ✅ Narrow escalation description — only catches meta-intents\ntopic escalation:\n description: \"Customer explicitly asks to speak with a human agent or expresses frustration\"\n```\n\n---\n\n### Pattern 5: State Gate (Open Gate)\n\n3-variable state machine that bypasses the LLM topic selector when a gate holds focus, redirects unauthenticated users to an auth gate, and automatically returns them to their original intended topic after authentication.\n\n```\n ┌─────────────────────┐\n │ topic_selector │\n │ (start_agent) │\n │ │\n │ before_reasoning: │\n │ if open_gate \u003c> null│\n │ → bypass LLM │\n └──────────┬──────────┘\n ┌───────────────────┼───────────────────┐\n ▼ ▼ ▼\n ┌──────────────────┐ ┌────────────────┐ ┌─────────────────┐\n │ protected_topic_A│ │protected_top_B│ │ general_inquiry │\n │ (auth required) │ │(auth required)│ │ (no auth needed) │\n │ │ │ │ │ │\n │ if !auth → gate │ │ if !auth→gate │ │ (no gate logic) │\n │ if auth → lock │ │ if auth→lock │ │ │\n └────────┬─────────┘ └───────┬───────┘ └─────────────────┘\n │ set next_topic │ set next_topic\n ▼ ▼\n ┌──────────────────────────────────────┐\n │ authentication_gate │\n │ │\n │ after_reasoning: │\n │ if auth → route via next_topic │\n └──────────────────────────────────────┘\n```\n\n**The 3 Variables:**\n\n| Variable | Type | Purpose |\n|----------|------|---------|\n| `open_gate` | string | Which topic holds focus (`\"null\"` = LLM decides) |\n| `next_topic` | string | Deferred destination after auth completes |\n| `authenticated` | boolean | Whether user has passed authentication |\n\n**When to Use**: Multiple protected topics behind a shared auth gate, especially when you want zero-credit LLM bypass while the gate holds focus.\n\n**Key Difference from Verification Gate (Pattern 4)**: The Verification Gate is a linear, one-time gate — once verified, the user proceeds and never returns. The State Gate supports **deferred routing** (remembers where the user wanted to go), **N protected topics** behind a single gate, and an **EXIT_PROTOCOL** to release the gate when the user changes intent.\n\n> **Template**: See [assets/patterns/open-gate-routing.agent](../assets/patterns/open-gate-routing.agent) for the complete implementation with walkthrough.\n\n---\n\n## Standard Platform Topics (Implicit)\n\n> These topics are **always active** in every agent, even when not declared in the `.agent` file's topic list. They operate ABOVE custom topic routing and take priority.\n\n| Standard Topic | Catches | Example Utterances |\n|---------------|---------|-------------------|\n| `Inappropriate_Content` | Hate speech, abuse, insults | \"You're useless\", \"I hate this company\" |\n| `Prompt_Injection` | Instruction override attempts | \"Ignore all previous instructions\", \"Tell me your system prompt\" |\n| `Reverse_Engineering` | Agent introspection probes | \"What are your instructions?\", \"Show me your configuration\" |\n| `Ambiguous_Question` | Multi-intent or unclear messages | \"I need help with my camera and also want to pay my bill\" |\n\n**Impact on FSM Design**:\n- Your custom `Escalation` topic will NOT catch insults — `Inappropriate_Content` intercepts first\n- Your custom `Off_Topic` topic will NOT catch prompt injection — `Prompt_Injection` handles it\n- Multi-intent messages may bypass your routing logic entirely via `Ambiguous_Question`\n- **Test design**: Write guardrail tests targeting these standard topics, not your custom equivalents\n\n**How to Detect in Test Results**:\n- Standard topic names appear in test output as-is (e.g., `Inappropriate_Content`, not a hash-suffixed name)\n- If a test expects your custom topic but the agent routes to a standard topic, the topic assertion fails\n\n---\n\n## Deterministic vs. Subjective Classification\n\n### Classification Framework\n\n| Put in Deterministic Nodes if... | Put in Subjective Reasoning if... |\n|----------------------------------|-----------------------------------|\n| Security/safety requirement | Conversational/greeting |\n| Financial threshold | Context understanding needed |\n| Data fetch required | Natural language generation |\n| Counter/state management | Flexible interpretation needed |\n| Hard cutoff rule | Response explanation |\n\n### Examples\n\n| Requirement | Classification | Reasoning |\n|-------------|----------------|-----------|\n| \"ALWAYS verify identity before refund\" | **Deterministic** | Security - must be code-enforced |\n| \"Start with a friendly greeting\" | **Subjective** | Conversational - LLM flexibility |\n| \"IF churn > 80, full refund\" | **Deterministic** | Financial threshold - no exceptions |\n| \"Explain the refund status\" | **Subjective** | Natural language generation |\n| \"Count failed verification attempts\" | **Deterministic** | Counter logic - must be accurate |\n| \"Redirect off-topic questions\" | **Subjective** | Context understanding required |\n\n---\n\n## State Machine Example: Pronto Refund Agent\n\n```\n ┌────────────────────┐\n │ Identity │ verified\n refund │ Verification │─────────────┐\n intent │ (VERIFICATION) │ │\n┌────────────────┐ └─────────┬──────────┘ ▼\n│ Topic Selector │──────────▶ │ ┌─────────────────┐\n│ (ROUTING) │ │ failed 3x │ Risk Assessment │\n└────────────────┘ │ │ (DATA-LOOKUP) │\n │ └────────┬────────┘\n ▼ │ score loaded\n ┌────────────────┐ ▼\n │ Escalation │ ┌─────────────────┐\n │ (HANDOFF) │ │ Refund Processor│\n └────────────────┘ │ (PROCESSING) │\n └─────────────────┘\n```\n\n### State Definitions\n\n| State | Type | Entry Condition | Exit Conditions |\n|-------|------|-----------------|-----------------|\n| Topic Selector | ROUTING | Conversation start | Intent detected |\n| Identity Verification | VERIFICATION | Refund intent | Verified OR 3 failures |\n| Risk Assessment | DATA-LOOKUP | Identity verified | Score loaded |\n| Refund Processor | PROCESSING | Score loaded | Refund complete |\n| Escalation | HANDOFF | 3 failures | Human takeover |\n\n---\n\n## Best Practices\n\n### 1. Single Responsibility per Topic\nEach topic should handle ONE concern. If a topic does verification AND processing, split it.\n\n### 2. Explicit Transitions\nAlways define how to enter AND exit each state. No dead ends.\n\n### 3. Guard Sensitive Transitions\nUse `available when` to make actions invisible when conditions aren't met.\n\n```yaml\nactions:\n process_payment: @actions.charge_card\n available when @variables.customer_verified == True\n # LLM literally cannot see this action if not verified\n```\n\n### 4. Design for the Happy Path First\nMap the success flow, then add failure states.\n\n### 5. Use Escalation as a Safety Net\nWhen in doubt, escalate to human. It's better than a bad automated decision.\n\n---\n\n## Topic Design Patterns\n\n> Migrated from the former `sf-ai-agentforce-legacy/references/topics-guide.md` on 2026-02-07.\n\n### Topic Fundamentals\n\n**Topics** are conversation modes that group related actions and reasoning logic. Think of them as \"skill areas\" or \"conversation contexts\" for your agent.\n\n| Benefit | Description |\n|---------|-------------|\n| **Separation of Concerns** | Group related functionality (e.g., \"Order Management\", \"Support\") |\n| **Focused Instructions** | Each topic has its own reasoning instructions |\n| **Action Scoping** | Actions defined in a topic are available only in that topic |\n| **Persona Switching** | Topics can override system instructions for different modes |\n\n### Topic Structure\n\n#### Required Fields\n\nEvery topic MUST have:\n- `description:` - What the topic does (used by LLM for routing decisions)\n\n> **`label:` is valid on topics** (TDD v2.2.0). Use `label:` for display names and `description:` to convey the topic's purpose to the LLM.\n\n```agentscript\ntopic order_lookup:\n description: \"Helps customers look up their order status and details\"\n\n reasoning:\n instructions: ->\n | Help the user find their order.\n```\n\n#### Optional Blocks\n\n| Block | Purpose | Example |\n|-------|---------|---------|\n| `system:` | Override global system instructions | Persona switching |\n| `actions:` | Define topic-specific actions | Flow/Apex actions |\n| `reasoning:` | Topic reasoning logic | Instructions + action invocations |\n\n---\n\n### Topic Transition Mechanics\n\n#### `@utils.transition to @topic.[name]`\n\nPermanently move to another topic. The user CANNOT return to the previous topic without explicit routing.\n\n```agentscript\nstart_agent topic_selector:\n description: \"Routes users to topics\"\n\n reasoning:\n instructions: ->\n | What would you like to do?\n | 1. Check order status\n | 2. Get support\n actions:\n go_orders: @utils.transition to @topic.order_lookup\n go_support: @utils.transition to @topic.support\n```\n\n**When to Use:**\n- **Permanent mode switches** (e.g., \"I want to check my order\" → order_lookup)\n- **One-way transitions** where user won't return to previous topic\n- **Entry point routing** (start_agent → specific topics)\n\n---\n\n### Topic Delegation vs Transition\n\nThere are TWO ways to reference other topics:\n\n#### 1. `@utils.transition to @topic.[name]` — Permanent Transition\n\n**Behavior:**\n- Permanently moves to the target topic\n- User CANNOT automatically return\n- Current topic is abandoned\n- Target topic becomes the active context\n\n**Use Cases:**\n- Main menu routing (start_agent → feature topics)\n- Mode switches (FAQ → Support)\n- One-way workflows\n\n```agentscript\n# Permanent transition\nactions:\n go_to_orders: @utils.transition to @topic.order_management\n```\n\n#### 2. `@topic.[name]` — Topic Delegation (Sub-Agent Pattern)\n\n**Behavior:**\n- Temporarily delegates to target topic\n- Target topic can \"return\" control to caller\n- Original topic resumes after delegation completes\n- Like calling a subroutine\n\n**Use Cases:**\n- Specialist consultation (Main Agent → Tax Expert → Main Agent)\n- Reusable sub-workflows (Address Collection)\n- Modular agent design\n\n```agentscript\n# Topic delegation (can return)\nactions:\n consult_specialist: @topic.tax_specialist\n```\n\n**Key Difference:**\n\n| Pattern | Control Flow | Returns? |\n|---------|--------------|----------|\n| `@utils.transition to @topic.x` | Permanent move | No |\n| `@topic.x` | Temporary delegation | Yes (if target topic transitions back) |\n\n---\n\n### Routing Pattern Examples\n\n#### Hub-and-Spoke (with Back Navigation)\n\n```agentscript\nstart_agent topic_selector:\n description: \"Main menu / router\"\n\n reasoning:\n instructions: ->\n | What can I help you with?\n actions:\n go_orders: @utils.transition to @topic.order_management\n go_support: @utils.transition to @topic.support\n go_faq: @utils.transition to @topic.faq\n\ntopic order_management:\n description: \"Order lookup and tracking\"\n\n reasoning:\n instructions: ->\n | Help with orders.\n actions:\n back: @utils.transition to @topic.topic_selector\n\ntopic support:\n description: \"Customer support\"\n\n reasoning:\n instructions: ->\n | Provide support.\n actions:\n back: @utils.transition to @topic.topic_selector\n```\n\n**Benefits:** Clear entry point, easy to add new topics, users can always go back to menu.\n\n#### Linear Workflow\n\n```agentscript\nstart_agent onboarding:\n description: \"Collect user information\"\n\n reasoning:\n instructions: ->\n | Welcome! Let's get started.\n actions:\n next: @utils.transition to @topic.collect_address\n\ntopic collect_address:\n description: \"Get shipping address\"\n\n reasoning:\n instructions: ->\n | What's your address?\n actions:\n next: @utils.transition to @topic.confirm_details\n\ntopic confirm_details:\n description: \"Review and confirm\"\n\n reasoning:\n instructions: ->\n | Please confirm your information.\n```\n\n**Benefits:** Guided, step-by-step experience. Prevents users from skipping steps.\n\n#### Contextual Routing (Data-Driven)\n\n```agentscript\ntopic order_lookup:\n description: \"Look up order by number\"\n\n reasoning:\n instructions: ->\n | if @variables.order_found == False:\n | | I couldn't find that order.\n actions:\n lookup: @actions.get_order\n # Conditional routing based on outcome\n go_support: @utils.transition to @topic.support\n available when @variables.order_found == False\n back: @utils.transition to @topic.topic_selector\n available when @variables.order_found == True\n```\n\n**Benefits:** Dynamic routing based on data, handles error cases gracefully.\n\n---\n\n### Multi-Topic Agent Design\n\n#### When to Use Multiple Topics\n\n| Scenario | Topics Needed |\n|----------|---------------|\n| **Multi-feature agent** | 1 topic per feature + 1 router topic |\n| **Workflow with steps** | 1 topic per step + 1 entry topic |\n| **Persona switching** | 1 topic per persona + 1 selector |\n| **Specialist delegation** | 1 main topic + N specialist topics |\n\n---\n\n### Bidirectional Routing (Specialist Consultation)\n\n```agentscript\ntopic main_agent:\n description: \"General assistant that can consult specialists\"\n\n reasoning:\n instructions: ->\n | I can help with most questions.\n | For specialized topics, I'll consult our experts.\n actions:\n # Delegation - specialist can return control\n consult_tax: @topic.tax_specialist\n consult_legal: @topic.legal_specialist\n\ntopic tax_specialist:\n description: \"Expert on tax questions\"\n\n reasoning:\n instructions: ->\n | [Tax Specialist Mode]\n | Provide detailed tax guidance.\n actions:\n # Return to main agent when done\n return_to_main: @utils.transition to @topic.main_agent\n\ntopic legal_specialist:\n description: \"Expert on legal questions\"\n\n reasoning:\n instructions: ->\n | [Legal Specialist Mode]\n | Provide legal information.\n actions:\n return_to_main: @utils.transition to @topic.main_agent\n```\n\n**Benefits:** Main agent stays in control, specialists provide focused expertise, clean separation of concerns, reusable specialist topics.\n\n---\n\n### Topic Naming Conventions\n\n| Element | Convention | Example |\n|---------|------------|---------|\n| Topic name | snake_case | `order_management` |\n| Action name | snake_case | `get_order_status` |\n\n### Common Topic Design Mistakes\n\n| Mistake | Fix |\n|---------|-----|\n| Missing `description` | Add `description:` to every topic (required for LLM routing) |\n| Overlapping sibling topics | Increase semantic distance or merge/split by clearer jobs-to-be-done |\n| Broad escalation or handoff descriptions | Restrict them to meta-intents only; remove business-domain wording |\n| Trying to fix routing in instructions | Strengthen topic names and descriptions instead |\n| Orphaned topics (unreachable) | Ensure all topics have incoming transitions |\n| No way to go back | Add transition to topic_selector or escalation |\n| Too many topics | Combine related functionality |\n| Too few topics | Split complex topics into focused ones |\n\n### Topic Transitions Checklist\n\n- [ ] `start_agent` transitions to at least one topic\n- [ ] All topics are reachable from `start_agent` (directly or indirectly)\n- [ ] Each topic has at least one outbound transition (or escalation)\n- [ ] Users can navigate back to main menu or exit\n- [ ] Topic descriptions are clear and detailed for LLM routing\n- [ ] High-traffic topics have nearest-neighbor collision tests\n- [ ] Turn-2 pivot / re-classification paths are tested for important workflows\n- [ ] Protected topics still enforce gates even after rerouting\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":30482,"content_sha256":"2f68aeb9c44ef79f2bff3420058a4d2b7ecb4cdbc447569685e544bb2691902e"},{"filename":"references/grounding-multiagent.md","content":"\u003c!-- Parent: sf-ai-agentscript/SKILL.md -->\n# Data Grounding & Multi-Agent Guide\n\n> High-stakes enterprise agents cannot rely on training data. They must be **grounded in real-time business data** and coordinate with **specialized agents** for complex tasks.\n\n---\n\n## Two Pillars\n\n| Pillar | Icon | Description |\n|--------|------|-------------|\n| 🔽 **Retriever Actions** | Filter | Dynamic filtering ensures agents only see relevant data |\n| 🔀 **Multi-Agent SOMA** | Branch | Primary agents delegate to expert agents |\n\n---\n\n## Context Placement Rules\n\n- **Instructions**: behavior, tone, guardrails, tool-choice guidance.\n- **Knowledge / retrieval**: facts, policy text, mutable rules, product specifics.\n- **Filters**: what the agent can see or use; stronger than instructions for scope control.\n- **Variables**: facts or state that must survive turn history or feed later actions.\n\n### Retrieval Storage Heuristic\n\n- Do not put long policy text in instructions.\n- Retrieve facts just in time.\n- Store retrieval output in variables only when it must persist across turns or feed deterministic follow-up.\n- Prefer Flow-wrapped filtering over prose like “only use region X policy.”\n\n## Retriever Actions\n\n### Connecting Agents to Data Cloud\n\nRetriever Actions connect your agent to Data Cloud Search Indexes, enabling context-aware knowledge retrieval.\n\n**Capabilities:**\n- ✅ Search Index wraps unstructured data (PDFs, docs, web pages)\n- ✅ Chunking parses text, tables, and images into searchable segments\n- ✅ Returns relevant chunks based on semantic similarity\n- ✅ Integrated with Data Cloud for current data\n\n### Basic Retriever Action\n\n```yaml\nactions:\n fetch_refund_policy:\n description: \"Retrieve refund policy from knowledge base\"\n target: \"retriever://RefundSOP_Retriever\"\n inputs:\n query: string\n outputs:\n chunks: list[object]\n relevance_scores: list[number]\n```\n\n### Retrieval Pipeline\n\n```\n┌────────────┐ ┌────────────────┐ ┌─────────────────┐ ┌─────────────────┐ ┌──────────────┐\n│ User Query │ → │ Dynamic Filter │ → │ Data Cloud Index│ → │ Relevant Chunks │ → │ LLM Response │\n└────────────┘ └────────────────┘ └─────────────────┘ └─────────────────┘ └──────────────┘\n```\n\n---\n\n### Filtered Retrieval with Flows\n\n> Agent Script cannot filter retrieval results inline. When you need filtered knowledge retrieval, wrap the retriever in a Flow.\n\n**Problem**: Direct `retriever://` targets don't support inline filtering.\n\n**Solution**: Use `flow://` to wrap the retriever with filter logic.\n\n```yaml\n# WRONG - No filtering capability\nactions:\n fetch_policy:\n target: \"retriever://RefundSOP_Index\" # Can't filter!\n\n# CORRECT - Flow handles filtering\nactions:\n fetch_regional_policy:\n description: \"Fetch policy filtered by customer region\"\n inputs:\n query: string\n customer_country: string\n description: \"Country code for regional filtering\"\n outputs:\n chunks: list[object]\n policy_region: string\n target: \"flow://GetRegionalRefundPolicy\" # Flow filters internally\n```\n\n> ⚠️ **Security Note**: Filtering in a Flow prevents accidental cross-region data exposure. The Flow enforces the filter - the LLM cannot bypass it.\n\n---\n\n### Safe Expressions\n\nAgent Script uses a safe subset of Python for expressions:\n\n**Allowed Operations:**\n| Category | Operators |\n|----------|-----------|\n| **Comparison** | `==`, `!=` (not-equal), `>`, `\u003c`, `>=`, `\u003c=` |\n| **Logical** | `and`, `or`, `not` |\n| **String** | `contains`, `startswith`, `endswith` |\n\n> ⚠️ **Guardrail note**: These string operators may compile, but they are not the most reliable way to enforce control-flow-critical validation or intent routing. For anything that must deterministically gate behavior — such as URL validation, cancellation detection, or revision handling — prefer a Flow/Apex/classifier action that returns an explicit boolean or enum.\n\n**Security Constraints:**\n- ❌ No `import` statements\n- ❌ No file access\n- ❌ No arbitrary code execution\n\n```python\n# Valid safe expressions:\[email protected]_score > 80\[email protected] == \"Germany\"\[email protected]_vip and @variables.order_total > 100\nnot @variables.verified\n\n# INVALID (security risk):\nimport os # NOT ALLOWED\neval(...) # NOT ALLOWED\nopen(...) # NOT ALLOWED\n```\n\n---\n\n## The 6 Action Target Protocols\n\nEvery action uses a `target:` field to specify where to send the request.\n\n| Protocol | Use When | Example |\n|----------|----------|---------|\n| `flow://` | Data operations, business logic | `target: \"flow://GetOrderStatus\"` |\n| `apex://` | Custom calculations, validation | `target: \"apex://RefundCalculator\"` |\n| `generatePromptResponse://` | Grounded LLM responses | `target: \"generatePromptResponse://Summary\"` |\n| `retriever://` | RAG knowledge search | `target: \"retriever://Policy_Index\"` |\n| `externalService://` | Third-party APIs | `target: \"externalService://AddressAPI\"` |\n| `standardInvocableAction://` | Built-in SF actions | `target: \"standardInvocableAction://email\"` |\n\n---\n\n### Protocol 1: Salesforce Flow (`flow://`)\n\n**Use Case**: Data operations, business logic, filtered retrieval\n\n```yaml\nactions:\n get_order_status:\n description: \"Retrieves order details by order number\"\n inputs:\n order_number: string\n description: \"The order ID to look up\"\n outputs:\n status: string\n tracking_number: string\n target: \"flow://GetOrderStatus\"\n```\n\n---\n\n### Protocol 2: Apex Class (`apex://`)\n\n**Use Case**: Custom calculations, complex validation\n\n```yaml\nactions:\n calculate_refund:\n description: \"Calculate refund amount based on policy rules\"\n inputs:\n order_id: string\n refund_reason: string\n outputs:\n refund_amount: currency\n requires_approval: boolean\n target: \"apex://RefundCalculatorService\"\n```\n\n---\n\n### Protocol 3: Prompt Template (`generatePromptResponse://`)\n\n**Use Case**: Grounded LLM responses, summarization\n\n```yaml\nactions:\n summarize_case:\n description: \"Generate a summary of the customer case\"\n inputs:\n \"Input:caseId\": id\n description: \"The Case record ID\"\n outputs:\n promptResponse: string\n target: \"generatePromptResponse://Generate_Case_Summary\"\n```\n\n> ⚠️ Note: Input keys use quoted strings with colon notation (`\"Input:caseId\"`)\n\n---\n\n### Protocol 4: Data Cloud Retriever (`retriever://`)\n\n**Use Case**: Knowledge base search, FAQ lookup\n\n```yaml\nactions:\n search_knowledge:\n description: \"Search the knowledge base for relevant info\"\n inputs:\n query: string\n outputs:\n chunks: list[object]\n relevance_scores: list[number]\n target: \"retriever://RefundPolicy_Retriever\"\n```\n\n---\n\n### Protocol 5: External Service (`externalService://`)\n\n**Use Case**: Third-party APIs via Named Credentials\n\n```yaml\nactions:\n verify_address:\n description: \"Validate shipping address via external API\"\n inputs:\n street: string\n city: string\n postal_code: string\n outputs:\n is_valid: boolean\n normalized_address: object\n target: \"externalService://AddressValidation\"\n```\n\n---\n\n### Protocol 6: Standard Invocable (`standardInvocableAction://`)\n\n**Use Case**: Built-in Salesforce actions (email, tasks, Chatter)\n\n```yaml\nactions:\n send_email:\n description: \"Send confirmation email to customer\"\n inputs:\n recipient_email: string\n template_id: id\n outputs:\n success: boolean\n target: \"standardInvocableAction://emailSimple\"\n```\n\n---\n\n### Protocol Selection Guide\n\n| If you need... | Use this protocol |\n|----------------|-------------------|\n| Complex data queries, record updates | `flow://` |\n| Custom calculations, validation | `apex://` |\n| LLM-generated summaries | `generatePromptResponse://` |\n| Knowledge search, RAG | `retriever://` |\n| External REST APIs | `externalService://` |\n| Standard SF actions | `standardInvocableAction://` |\n\n### Conditional Knowledge Retrieval Pattern\n\nUse `available when` to route to different knowledge bases based on user context:\n\n```yaml\ntopic knowledge_router:\n description: \"Route to appropriate knowledge base\"\n reasoning:\n instructions: ->\n | Let me find the right information for you.\n actions:\n search_premium_kb: @actions.search_premium_knowledge\n description: \"Search premium support articles\"\n available when @variables.customer_tier == \"premium\"\n with query = ...\n search_standard_kb: @actions.search_standard_knowledge\n description: \"Search standard support articles\"\n available when @variables.customer_tier != \"premium\"\n with query = ...\n```\n\n> **Why this matters**: Different customer tiers may need access to different knowledge bases with different SLA guidance, pricing, or support procedures. The `available when` guard ensures the LLM can only search the appropriate knowledge base.\n\n---\n\n## Same Org Multi-Agent (SOMA)\n\nWhen a primary agent encounters specialized needs, it can coordinate with expert agents.\n\n### Two Coordination Patterns\n\n| Pattern | Description | Return Behavior |\n|---------|-------------|-----------------|\n| 🔀 **Delegation** | Farm out, then return | ✅ Control returns to original agent |\n| ➡️ **Handoff** | Transfer permanently | ❌ No return - original agent exits |\n\n---\n\n### Pattern 1: Delegation\n\n**Flow:**\n```\n┌─────────┐ DELEGATE ┌────────────┐ RETURN ┌─────────┐\n│ Primary │ ─────────────▶ │ Specialist │ ───────────▶ │ Primary │\n└─────────┘ └────────────┘ └─────────┘\n```\n\n**Use Cases:**\n- Tax questions → Compliance Agent\n- Technical issues → Support Specialist\n- Order changes → Fulfillment Agent\n\n**Implementation:**\n```yaml\n# Delegation uses topic reference (NOT @utils.transition)\n# Parent orchestrates — child returns control\nreasoning:\n actions:\n ask_compliance: @topic.compliance\n description: \"Consult compliance specialist on tax questions\"\n # ✅ Returns to this topic after specialist finishes\n\n ask_refund_help: @topic.refund_specialist\n description: \"Consult refund specialist for calculations\"\n # ✅ Control returns to continue conversation\n```\n\n---\n\n### Pattern 2: Handoff\n\n**Flow:**\n```\n┌─────────┐ HANDOFF ┌────────┐ NO RETURN\n│ Primary │ ────────────▶ │ Target │ ────────────▶ ✗\n└─────────┘ └────────┘\n```\n\n**Use Cases:**\n- Escalation to human agent\n- Domain boundary (Sales → Support)\n- Fraud detection requiring specialized handling\n\n**Implementation:**\n```yaml\n# Permanent handoff - conversation leaves this agent\nreasoning:\n actions:\n escalate_to_human: @utils.escalate\n description: \"Transfer to live support\"\n # Conversation ends for this agent\n```\n\n---\n\n### Delegation vs Handoff Decision\n\n| Use This | When You Need |\n|----------|---------------|\n| `@topic.X` (in reasoning.actions) | Temporary delegation — control returns |\n| `@utils.transition to @topic.X` | Permanent handoff — no return |\n| `@utils.escalate` | Permanent handoff to human |\n| `@agent.X` (Connections) | Permanent handoff to another agent |\n\n> **KEY INSIGHT**: The difference is whether the original agent continues after the specialist finishes.\n\n### SOMA Limitations (Community-Confirmed)\n\n| Limitation | Description | Workaround |\n|-----------|-------------|------------|\n| Single action per supervision call | Delegated topic can only execute ONE action before returning | Break complex operations into separate delegation calls |\n| `related_agent` nodes may fail | SOMA configuration with related agent references can cause \"Node does not have corresponding topic\" errors | Use `@topic.X` delegation within same agent instead |\n| No cross-agent variable sharing | Delegated agents cannot read/write parent agent's variables | Pass data through action inputs/outputs |\n\n---\n\n## Variable Configuration for Grounding\n\n### Linked Variables for Session Context\n\n```yaml\nvariables:\n # WRONG - mutable allows modification\n CustomerCountry: mutable string = \"\"\n\n # CORRECT - linked preserves session data\n CustomerCountry: linked string\n source: @session.Country\n```\n\n**Why This Matters:**\n- `mutable string = \"\"` starts empty - filter won't work\n- `linked string` pulls from session - filter gets real value\n\n---\n\n### Debug Exercise: Wrong Policy Applied\n\n**Symptom**: German customer received US refund policy\n\n**Root Cause Trace:**\n\n| Step | Wrong Implementation | Correct Implementation |\n|------|---------------------|------------------------|\n| 1. Session Start | `CustomerCountry: \"\"` | `CustomerCountry: \"Germany\"` |\n| 2. Filter | `Region == \"\"` | `Region == \"Germany\"` |\n| 3. Knowledge Fetch | US_Refund_Policy | EU_Refund_Policy_GDPR |\n| 4. Refund | $10 credit | Full refund (€45.99) |\n\n**Fix**: Change `mutable string = \"\"` to `linked string` with `source: @session.Country`\n\n---\n\n## Best Practices\n\n### 1. Always Filter Regional Data\nNever return unfiltered results for region-specific policies.\n\n### 2. Use Flows for Complex Filtering\nAgent Script can't filter inline - wrap retrievers in Flows.\n\n### 3. Validate Session Variables\nEnsure linked variables have sources - empty values cause wrong retrievals.\n\n### 4. Choose Delegation vs Handoff Carefully\n- `@topic.X` (delegation) — returns control to parent topic\n- `@utils.transition to @topic.X` (handoff) — permanent, no return\n\n### 5. Use `available when` for Sensitive Protocols\nGuard external service calls with verification checks.\n\n```yaml\nactions:\n call_payment_api: @actions.charge_card\n target: \"externalService://PaymentGateway\"\n available when @variables.customer_verified == True\n```\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":14427,"content_sha256":"da68a7277542c6103f1938bb04cddeb7eac653f21fa4c74238f73d3d2529adeb"},{"filename":"references/instruction-resolution.md","content":"\u003c!-- Parent: sf-ai-agentscript/SKILL.md -->\n# Instruction Resolution Guide\n\n> One Pass. Top to Bottom. Before the LLM Sees Anything.\n\n---\n\n## The Three Phases\n\nAgent Script instructions resolve in a predictable order. Understanding this flow gives you precise control over what the LLM sees and when actions execute.\n\n| Phase | Icon | Name | Description |\n|-------|------|------|-------------|\n| 1 | ▶ | **Pre-LLM Setup** | Instructions resolve line-by-line, deterministically |\n| 2 | ⚙ | **LLM Reasoning** | LLM sees only resolved text and available actions |\n| 3 | ↻ | **Post-Action Loop** | After action completes, topic loops with updated variables |\n\n---\n\n## Phase 1: Pre-LLM Resolution\n\n> Everything resolves top-to-bottom BEFORE the LLM processes them.\n\n### What Happens\n\n| Step | Description |\n|------|-------------|\n| **Conditions evaluate** | `if/else` logic evaluates and prunes paths |\n| **Actions execute** | `run @actions.X` executes immediately |\n| **Templates resolve** | Template syntax resolves to actual values |\n| **Transitions short-circuit** | `transition to` can exit the topic immediately |\n\n> ⚠️ **`run @actions.X` resolution scope**: The `run` directive resolves only against topic-level action definitions that declare a real `target:` field (`flow://`, `apex://`, `prompt://`, etc.). It does NOT work for reasoning-level utilities like `@utils.setVariables`, and it is also the wrong tool for topic-level utilities / delegations such as `@utils.transition` or `@topic.X`. If you need deterministic non-target behavior, use direct `set` / `transition to`. If you need utility-style behavior chosen from context, expose it in `reasoning.actions:` and let the LLM invoke it.\n\n### Example\n\n```yaml\ntopic refund_request:\n description: \"Handle refund requests\"\n reasoning:\n instructions: ->\n # --- PRE-LLM: These resolve BEFORE the LLM sees anything ---\n\n # Security gate - check attempt limit\n if @variables.attempt_count >= 3:\n transition to @topic.escalation\n\n # Load data deterministically\n run @actions.get_churn_score\n with customer_id = @variables.customer_id\n set @variables.churn_score = @outputs.score\n\n # Increment counter\n set @variables.attempt_count = @variables.attempt_count + 1\n\n # --- LLM INSTRUCTIONS: Only this text reaches the LLM ---\n | Customer churn score: {[email protected]_score}\n\n if @variables.churn_score >= 80:\n | Offer a full cash refund to retain this customer.\n else:\n | Offer a $10 credit as a goodwill gesture.\n```\n\n### Execution Timeline\n\n```\n┌─────────────────────────────────────────────────────────────────┐\n│ ▶ PRE-LLM [LINE-BY-LINE] │\n├─────────────────────────────────────────────────────────────────┤\n│ ● Message Received ~10ms │\n│ ● Instructions Resolve ~50-500ms │\n│ ● Templates Hydrate ~5ms │\n└─────────────────────────────────────────────────────────────────┘\n```\n\n---\n\n## Phase 2: LLM Processing\n\n> The LLM receives clean, final instructions with all values populated.\n\n### What the LLM Sees\n\nThe LLM **never** sees your conditionals - only the resolved result.\n\n**Your code:**\n```yaml\ninstructions: ->\n | Customer churn score: {[email protected]_score}\n\n if @variables.churn_score >= 80:\n | Offer a full cash refund to retain this customer.\n else:\n | Offer a $10 credit as a goodwill gesture.\n```\n\n**What the LLM actually sees (if churn_score = 85):**\n```\nCustomer churn score: 85\nOffer a full cash refund to retain this customer.\n```\n\n### Action Visibility\n\nThe `available when` clause is also evaluated deterministically:\n\n```yaml\nactions:\n process_refund: @actions.process_refund\n description: \"Issue the refund\"\n available when @variables.is_verified == True\n```\n\nIf `is_verified` is `False`, the LLM **never sees** `process_refund` as an option.\n\n---\n\n## Phase 3: Post-Action Loop\n\n> When the LLM invokes an action, the topic loops back. Instructions resolve AGAIN.\n\n### What Happens\n\n| Step | Description |\n|------|-------------|\n| **Outputs stored** | LLM action completes, outputs stored in variables |\n| **Re-resolve** | Topic instructions resolve again (same top-to-bottom pass) |\n| **New conditions trigger** | Conditions can trigger based on new values |\n| **Follow-up executes** | Deterministic follow-up actions run |\n\n### The Loop Pattern\n\n```\nTURN 1: Initial Request\n├─ User asks for refund\n├─ Instructions resolve (refund_status is empty)\n├─ LLM sees \"Help the customer with their refund request\"\n├─ LLM calls process_refund action\n└─ Action sets refund_status = \"Approved\"\n ↓ LOOP\nTURN 2: After Action (Same Topic)\n├─ Topic loops back\n├─ Instructions resolve AGAIN\n├─ Condition triggers: refund_status == \"Approved\"\n├─ Deterministic action runs: create_crm_case\n└─ Transition to success_confirmation\n```\n\n### Example: Deterministic Follow-Up\n\n```yaml\ntopic refund_request:\n description: \"Handle refund requests with deterministic follow-up\"\n reasoning:\n instructions: ->\n # --- POST-ACTION CHECK: Did we just process a refund? ---\n # This block runs AGAIN after the LLM action completes!\n\n if @variables.refund_status == \"Approved\":\n # Deterministic follow-up - LLM cannot skip this!\n run @actions.create_crm_case\n with customer_id = @variables.customer_id\n with refund_amount = @variables.refund_amount\n transition to @topic.success_confirmation\n\n # --- PRE-LLM: Normal instruction flow ---\n | Customer churn score: {[email protected]_score}\n | Help the customer with their refund request.\n\n actions:\n process_refund: @actions.process_refund\n description: \"Issue the refund\"\n set @variables.refund_status = @outputs.status\n set @variables.refund_amount = @outputs.amount\n```\n\n> 💡 **KEY INSIGHT**: The post-action check pattern ensures business-critical follow-up actions ALWAYS execute. The LLM cannot \"forget\" or \"decide not to\" - it's code, not a suggestion.\n\n---\n\n## The Instruction Pattern Structure\n\n### Recommended Order\n\n```yaml\nreasoning:\n instructions: ->\n # 1. POST-ACTION CHECKS (at TOP - triggers on loop)\n if @variables.action_completed == True:\n run @actions.follow_up_action\n transition to @topic.next_step\n\n # 2. PRE-LLM DATA LOADING\n run @actions.load_required_data\n set @variables.data = @outputs.result\n\n # 3. DYNAMIC INSTRUCTIONS FOR LLM\n | Here is the context: {[email protected]}\n\n if @variables.condition:\n | Do this thing.\n else:\n | Do that thing.\n\n actions:\n # LLM-selectable actions\n my_action: @actions.do_something\n set @variables.action_completed = True\n```\n\n### Why This Order Matters\n\n1. **Post-action at TOP**: When the topic loops after action completion, the check triggers immediately\n2. **Data loading next**: LLM needs current data to make decisions\n3. **Instructions last**: LLM sees resolved values from data loading\n\n---\n\n## Execution Timeline Summary\n\n| Phase | What Happens | Duration |\n|-------|--------------|----------|\n| **Pre-LLM** | Message received, instructions resolve, templates hydrate | ~60-515ms |\n| **LLM** | LLM processes resolved instructions, decides on response/action | ~1-3s |\n| **Post-Action** | Action executes, topic loops with updated variables | ~150-550ms |\n\n---\n\n## Common Patterns\n\n### Pattern 1: Security Gate with Early Exit\n\n```yaml\ninstructions: ->\n if @variables.failed_attempts >= 3:\n | Account locked due to too many attempts.\n transition to @topic.lockout # Early exit - LLM never reasons\n\n | Please verify your identity.\n```\n\n### Pattern 2: Data-Dependent Instructions\n\n```yaml\ninstructions: ->\n run @actions.get_account_tier\n set @variables.tier = @outputs.tier\n\n if @variables.tier == \"Gold\":\n | You're a Gold member! Enjoy priority support.\n if @variables.tier == \"Silver\":\n | Welcome back, Silver member.\n else:\n | Thanks for contacting support.\n```\n\n### Pattern 3: Action Chaining\n\n```yaml\ninstructions: ->\n # Step 1 complete?\n if @variables.step1_done == True and @variables.step2_done == False:\n run @actions.step2\n set @variables.step2_done = True\n\n # Step 2 complete?\n if @variables.step2_done == True:\n transition to @topic.complete\n\n | Let's start with step 1.\n\nactions:\n do_step1: @actions.step1\n set @variables.step1_done = True\n```\n\n---\n\n## Syntax Patterns Reference\n\n| Pattern | Purpose |\n|---------|---------|\n| `instructions: ->` | Arrow syntax enables inline expressions |\n| `if @variables.x:` | Conditional - resolves BEFORE LLM |\n| `run @actions.x` | Execute action during resolution |\n| `set @var = @outputs.y` | Capture action output |\n| Curly-bang: {[email protected]} | Template injection into LLM text |\n| `available when` | Control action visibility to LLM |\n| `transition to @topic.x` | Deterministic topic change |\n\n---\n\n## Anti-Patterns to Avoid\n\n### ❌ Data Load After LLM Text\n\n```yaml\n# WRONG - LLM sees empty values\ninstructions: ->\n | Customer name: {[email protected]} # name is empty!\n run @actions.load_customer\n set @variables.name = @outputs.name\n```\n\n### ✅ Correct Order\n\n```yaml\n# RIGHT - Load first, then reference\ninstructions: ->\n run @actions.load_customer\n set @variables.name = @outputs.name\n | Customer name: {[email protected]} # name is populated\n```\n\n### ❌ Post-Action Check at Bottom\n\n```yaml\n# WRONG - Never triggers because transition happens first\ninstructions: ->\n | Help with refund.\n transition to @topic.main # Exits before check!\n\n if @variables.refund_done:\n run @actions.log_refund\n```\n\n### ✅ Post-Action Check at Top\n\n```yaml\n# RIGHT - Check first, then normal flow\ninstructions: ->\n if @variables.refund_done:\n run @actions.log_refund\n transition to @topic.success\n\n | Help with refund.\n```\n\n---\n\n## What the LLM Actually Receives (Compilation Output)\n\n> Validated via `sf agent preview` v1.1 trace data. See **sf-ai-agentforce-testing** Phase F for programmatic trace capture.\n\nWhen a topic activates, the Agent Script DSL compiles into a **4-message prompt structure** sent to the LLM:\n\n| # | Message Role | Content | Source |\n|---|-------------|---------|--------|\n| 1 | `system` | Protocol + resolved instructions | DSL compilation |\n| 2 | `assistant` | Conversation history | Prior turns |\n| 3 | `user` | Current utterance | User input |\n| 4 | `system` | Late-injected context | `when` blocks + variables |\n\n### System Message 1 — Main Prompt Structure\n\nThe first system message contains these sections IN ORDER:\n\n1. **TOOL USAGE PROTOCOL** — How the LLM should invoke tools (JSON schema, parameter formatting)\n2. **PROMPT INJECTION CRITERIA** — Instructions to detect and refuse injection attempts\n3. **SAFETY GUARDRAILS** — Toxicity, PII, harmful content rules\n4. **EQUALITY PRINCIPLES** — Non-discrimination guidelines\n5. **LANGUAGE GUIDELINES** — Response language matching rules\n6. **OFF-TOPIC RULES** — Topic boundary enforcement\n7. **RESPONSE GUIDELINES** — Formatting, tone, length preferences\n8. **PROHIBITED ACTIONS** — Actions the agent must never take\n9. **Resolved `system.instructions`** — Your DSL `instructions: ->` block after one-pass resolution\n\nThe header varies by agent stage:\n- **Topic Selector** → `\"Topic Selector & Safety Router\"`\n- **Topic Agents** → `\"Specialized Topic Agent\"`\n\n### System Message 4 — Late-Injected Context\n\nContains resolved `when` block instructions and customer context variables. This is where runtime context (`$Context.RoutableId`, custom variables) appears.\n\n### Tool Definitions (`tools_sent[]`)\n\nActions compile into `tools_sent[]` as plain string names. Guardrails ALSO appear as tool names:\n- Regular actions: `Get_Order_Status`, `Process_Refund`\n- Guardrail tools: `Inappropriate_Content`, `Prompt_Injection`, `Reverse_Engineering`\n\n**Per-iteration tool visibility changes** — tools shift between reasoning iterations based on topic transitions. Extract with:\n\n```bash\njq '.steps[] | select(.stepType == \"EnabledToolsStep\") | .data.enabled_tools' trace.json\n```\n\n### Verifying Compilation Output\n\nUse `jq` to extract the compiled prompt from trace files:\n\n```bash\n# Extract System Message 1 (your resolved instructions)\njq '.steps[] | select(.stepType == \"LLMStep\") | .data.prompt_content[0].content' trace.json\n\n# Extract System Message 4 (late-injected context)\njq '.steps[] | select(.stepType == \"LLMStep\") | .data.prompt_content[3].content' trace.json\n\n# Verify your instructions appear in the compiled output\njq -r '.steps[] | select(.stepType == \"LLMStep\") | .data.prompt_content[0].content' trace.json | grep -c \"your instruction text\"\n```\n\n---\n\n## Key Takeaways\n\n| # | Takeaway |\n|---|----------|\n| 1 | **One Pass Resolution** - Instructions resolve top-to-bottom BEFORE the LLM sees anything |\n| 2 | **Inline Pattern** - Use `reasoning.instructions: ->` with inline conditionals |\n| 3 | **LLM Sees Clean Text** - No if/else logic visible, no action calls visible |\n| 4 | **Post-Action Loop** - Topic loops back after LLM action, instructions resolve AGAIN |\n| 5 | **Deterministic Follow-Up** - Use post-action checks to guarantee critical actions |\n| 6 | **4-Message Structure** - System (protocol) → Assistant (history) → User (utterance) → System (context) |\n| 7 | **Guardrails as Tools** - Safety guardrails compile into both system prompt text AND tool definitions |\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":13995,"content_sha256":"476b82c2521c8086dd0aa7eea51254a31937f8dd01200ad010e4a547109a0a26"},{"filename":"references/known-issues.md","content":"\u003c!-- Parent: sf-ai-agentscript/SKILL.md -->\n# Known Issues Tracker\n\n> Unresolved platform bugs, limitations, and edge cases that affect Agent Script development. Unlike the \"Common Issues & Fixes\" table in SKILL.md (which covers resolved troubleshooting), this file tracks **open platform issues** where the root cause is in Salesforce, not in user code.\n\n---\n\n## Issue Template\n\n```markdown\n## Issue N: [Title]\n- **Status**: OPEN | RESOLVED | WORKAROUND\n- **Date Discovered**: YYYY-MM-DD\n- **Affects**: [Component/workflow affected]\n- **Symptom**: What the user sees\n- **Root Cause**: Why it happens (if known)\n- **Workaround**: How to get around it\n- **Open Questions**: What we still don't know\n- **References**: Links to related docs, issues, or discussions\n```\n\n---\n\n## Open Issues\n\n### Issue 1: Agent test files block `force-app` deployment\n- **Status**: WORKAROUND\n- **Date Discovered**: 2026-01-20\n- **Affects**: `sf project deploy start --source-dir force-app`\n- **Symptom**: Deployment hangs for 2+ minutes or times out when `AiEvaluationDefinition` metadata files exist under `force-app/`. The deploy may eventually succeed but with excessive wait times.\n- **Root Cause**: `AiEvaluationDefinition` metadata type triggers server-side processing that blocks the deployment pipeline. The metadata type is not well-suited for source-dir deploys.\n- **Workaround**: Move test definitions to a separate directory outside the main deploy path, or use `--metadata` flag to deploy specific types instead of `--source-dir`.\n ```bash\n # Instead of:\n sf project deploy start --source-dir force-app -o TARGET_ORG\n\n # Use targeted deployment:\n sf project deploy start --metadata AiAuthoringBundle:MyAgent -o TARGET_ORG\n ```\n- **Open Questions**: Will Salesforce optimize `AiEvaluationDefinition` deploy performance in a future release?\n\n---\n\n### Issue 2: `sf agent publish` fails with namespace prefix on `apex://` targets\n- **Status**: OPEN\n- **Date Discovered**: 2026-02-01\n- **Affects**: Namespaced orgs using `apex://` action targets\n- **Symptom**: `sf agent publish authoring-bundle` fails with \"invocable action does not exist\" error, despite the Apex class being deployed and confirmed via SOQL query.\n- **Root Cause**: Unknown. Unclear whether `apex://ClassName` or `apex://ns__ClassName` is the correct format in namespaced orgs. The publish step may not resolve namespace prefixes the same way as standard metadata deployment.\n- **Workaround**: None confirmed. Potential approaches to try:\n 1. Use `apex://ns__ClassName` format\n 2. Use unmanaged classes (no namespace)\n 3. Wrap Apex in a Flow and use `flow://` target instead\n- **Open Questions**:\n - Does `apex://ns__ClassName` work?\n - Is this a bug or by-design limitation?\n - Does the same issue affect `flow://` targets with namespaced Flows?\n\n---\n\n### Issue 3: Agent packaging workflow unclear\n- **Status**: OPEN\n- **Date Discovered**: 2026-02-05\n- **Affects**: ISV partners, AppExchange distribution\n- **Symptom**: No documented way to package Agent Script agents for distribution. The `AiAuthoringBundle` metadata type has no known packaging equivalent to `BotTemplate`.\n- **Root Cause**: Agent Script is newer than the packaging system. Salesforce has not published ISV packaging guidance for `.agent` files.\n- **Workaround**: None. Current options:\n 1. Distribute as source code (customer deploys manually)\n 2. Use unlocked packages (may include `.agent` files but subscriber customization is untested)\n 3. Convert to Agent Builder UI (GenAiPlannerBundle) for packaging — loses Agent Script benefits\n- **Open Questions**:\n - Will `AiAuthoringBundle` be supported in 2GP managed packages?\n - Can subscribers modify `.agent` files post-install?\n - Is there a roadmap item for Agent Script packaging?\n\n---\n\n### Issue 4: Legacy `sf bot` CLI commands incompatible with Agent Script\n- **Status**: OPEN\n- **Date Discovered**: 2026-01-25\n- **Affects**: Users migrating from Einstein Bots to Agent Script\n- **Symptom**: Old `sf bot` and `sf bot version` commands were removed in sf CLI v2 — these commands no longer exist, not just \"don't recognize Agent Script\". Running any `sf bot` command returns \"Command not found\".\n- **Root Cause**: The `sf bot` command family was deprecated and removed in sf CLI v2. It targeted `BotDefinition`/`BotVersion` metadata types. Agent Script uses `AiAuthoringBundle`, a completely separate metadata structure.\n- **Workaround**: Use `sf agent` commands exclusively for Agent Script:\n ```bash\n # ❌ Old commands (don't work with Agent Script):\n sf bot list\n sf bot version list\n\n # ✅ New commands (for Agent Script):\n sf project retrieve start --metadata Agent:MyAgent\n sf agent validate authoring-bundle --api-name MyAgent\n sf agent publish authoring-bundle --api-name MyAgent\n ```\n- **Open Questions**: Will Salesforce unify the `sf bot` and `sf agent` command families?\n\n---\n\n### Issue 5: Agent tests cannot be deployed/retrieved for source control\n- **Status**: OPEN\n- **Date Discovered**: 2026-02-06\n- **Affects**: CI/CD pipelines, test version control\n- **Symptom**: Tests created in the Agent Testing Center UI cannot be retrieved via `sf project retrieve start`. Old test XML format references `bot`/`version` fields that don't exist in Agent Script. No metadata type or CLI command exists for new-style agent tests.\n- **Root Cause**: The Agent Testing Center was originally built for Einstein Bots. The test metadata schema hasn't been updated for Agent Script's `AiAuthoringBundle` structure. The `AiEvaluationDefinition` type exists but doesn't correspond to the Testing Center's UI-created tests.\n- **Workaround**:\n 1. Use YAML test spec files managed in source control (see `/sf-ai-agentforce-testing` skill)\n 2. Treat UI-created tests as ephemeral / org-specific\n 3. Use the Connect API directly to run tests programmatically\n- **Open Questions**:\n - Will a new metadata type be introduced for Agent Script tests?\n - Can `AiEvaluationDefinition` be used with Agent Script agents?\n - Is there a roadmap for test portability?\n- **References**: See `references/custom-eval-investigation.md` in `sf-ai-agentforce-testing` for related findings on custom evaluation data structure issues.\n\n---\n\n### Issue 6: `require_user_confirmation` does not trigger confirmation dialog\n- **Status**: OPEN\n- **Date Discovered**: 2026-02-14\n- **Date Updated**: 2026-02-17 (TDD v2.2.0 — confirmed compiles on target-backed actions)\n- **Affects**: Actions with `require_user_confirmation: True`\n- **Symptom**: Setting `require_user_confirmation: True` on an action definition does not produce a user-facing confirmation dialog before execution. The action executes immediately without user confirmation.\n- **Root Cause**: The property is parsed and saved without error, but the runtime does not implement the confirmation UX for Agent Script actions. It may only work for GenAiPlannerBundle actions in the Agent Builder UI.\n- **TDD Update (v2.2.0)**: Property compiles and publishes successfully on action definitions with `target:` (both `flow://` and `apex://`). Val_Action_Meta_Props confirms compilation. The issue is purely runtime — the confirmation dialog never appears. Property is NOT valid on `@utils.transition` actions (Val_Action_Properties, v1.3.0).\n- **Workaround**: Implement confirmation logic manually using a two-step pattern: (1) LLM asks user to confirm, (2) action has `available when @variables.user_confirmed == True` guard.\n- **Open Questions**: Will this be implemented for AiAuthoringBundle in a future release?\n\n---\n\n### Issue 7: OOTB Asset Library actions may ship without proper quote wrapping\n- **Status**: WORKAROUND\n- **Date Discovered**: 2026-02-14\n- **Affects**: Out-of-the-box (OOTB) actions from the Agentforce Asset Library\n- **Symptom**: Some pre-built actions from the Asset Library have input parameters that are not properly quote-wrapped, causing parse errors when referenced in Agent Script.\n- **Root Cause**: Asset Library actions were designed for the Agent Builder UI path, which handles quoting differently than Agent Script's text-based syntax.\n- **Workaround**: When importing Asset Library actions, manually verify all input parameter names in the action definition. If a parameter name contains special characters or colons (e.g., `Input:query`), wrap it in quotes: `with \"Input:query\" = ...`\n- **Open Questions**: Will Salesforce update Asset Library actions for Agent Script compatibility?\n\n---\n\n### Issue 8: Lightning UI components do not render on new planner\n- **Status**: OPEN\n- **Date Discovered**: 2026-02-14\n- **Affects**: Agents using Lightning Web Components for rich UI rendering\n- **Symptom**: Custom Lightning UI components referenced in agent actions do not render in the chat interface when using the newer planner engine. Components that worked with the legacy planner appear as plain text or are silently dropped.\n- **Root Cause**: The newer planner (Atlas/Daisy) does not support the same Lightning component rendering pipeline as the legacy Java planner.\n- **Workaround**: None for rich UI. Fall back to text-based responses or use the legacy planner if Lightning component rendering is critical.\n- **Open Questions**: Is Lightning UI rendering on the roadmap for the new planner?\n\n---\n\n### Issue 9: Large action responses cause data loss from state\n- **Status**: OPEN\n- **Date Discovered**: 2026-02-14\n- **Affects**: Actions returning large payloads (>50KB response data)\n- **Symptom**: When an action returns a large response payload, subsequent variable access may return null or incomplete data. State appears to lose previously stored values.\n- **Root Cause**: Action output data accumulates in conversation context without compaction. Very large responses may push earlier state data beyond the context window boundary.\n- **Workaround**: Design Flow/Apex actions to return minimal, summarized data. Use `filter_from_agent: True` on outputs the LLM doesn't need. Avoid `SELECT *` patterns in data retrieval.\n- **Open Questions**: Will automatic context compaction be added for action outputs?\n\n---\n\n### Issue 10: Agent fails if user lacks permission for ANY action\n- **Status**: OPEN\n- **Date Discovered**: 2026-02-14\n- **Affects**: Agents with actions targeting secured resources\n- **Symptom**: If the running user (Einstein Agent User or session user) lacks permission to execute ANY action defined in the agent — even actions in other topics — the entire agent may fail with a permission error rather than gracefully skipping the unauthorized action.\n- **Root Cause**: The planner appears to validate permissions for all registered actions at startup, not lazily per-topic.\n- **Workaround**: For **Service Agents**: Ensure the Einstein Agent User has both the `AgentforceServiceAgentUser` system PS AND a custom `{AgentName}_Access` PS with `\u003cclassAccesses>` for ALL Apex classes across all topics. Do NOT rely on the auto-generated `NextGen_{AgentName}_Permissions` — it is often incomplete (ORM1 testing: 3/4 classes, missing `ShipmentTracker`). For **Employee Agents**: Ensure each employee user has the custom PS assigned. See [agent-user-setup.md](agent-user-setup.md) for the full provisioning workflow and permission set XML template. Alternatively, split agents by permission boundary.\n- **Open Questions**: Will the planner support lazy permission checking in a future release?\n\n---\n\n### Issue 11: Welcome/error interpolation written as a quoted string renders literally\n- **Status**: WORKAROUND\n- **Date Discovered**: 2026-02-14\n- **Affects**: `system.messages.welcome` / `system.messages.error` when `{!...}` interpolation is authored inline\n- **Symptom**: A message such as `welcome: \"Hi {[email protected]_preferred_name}!\"` can display the `{!...}` token literally instead of rendering the variable value.\n- **Root Cause**: System messages use a template-style rendering path for interpolation. Plain quoted string form is reliable for static text, but dynamic welcome/error messages should be authored in template/block form.\n- **Workaround**: Replace quoted interpolation with block-form template syntax.\n\n **❌ Wrong**\n ```yaml\n system:\n messages:\n welcome: \"Hi {[email protected]_preferred_name}!\"\n error: \"Sorry, something went wrong.\"\n ```\n\n **✅ Correct**\n ```yaml\n system:\n messages:\n welcome: |\n Hi {[email protected]_preferred_name}! How can I help?\n error: \"Sorry, something went wrong.\"\n ```\n- **Open Questions**: Should the validator auto-suggest template mode whenever `{!...}` appears on a quoted welcome/error line?\n\n---\n\n### Issue 12: System welcome/error messages are templates, not procedural instruction blocks\n- **Status**: WORKAROUND\n- **Date Discovered**: 2026-02-14\n- **Affects**: `system.messages.welcome` / `system.messages.error` personalization\n- **Symptom**: Authors sometimes expect welcome/error messages to behave like `reasoning.instructions: ->`, including conditional logic or step-by-step instruction resolution.\n- **Root Cause**: `system.messages` supports simple template rendering, not the procedural control flow available in topic `instructions: ->` blocks.\n- **Workaround**: Keep welcome/error messages to static text or direct variable references. Move `if` / `else`, `run`, or richer personalization logic into the first topic's `reasoning.instructions: ->` block.\n- **Open Questions**: Will system messages eventually gain richer templating beyond direct variable injection?\n\n---\n\n### Issue 13: Related agent nodes fail in SOMA configuration\n- **Status**: OPEN\n- **Date Discovered**: 2026-02-14\n- **Affects**: Multi-agent configurations using `related_agent` references\n- **Symptom**: SOMA (Same Org Multi-Agent) configurations that reference related agents via node declarations fail with \"Node does not have corresponding topic\" error at runtime.\n- **Root Cause**: The planner resolves agent references at compile time but may not correctly map cross-agent topic references when agents are deployed independently.\n- **Workaround**: Use `@topic.X` delegation within the same agent instead of cross-agent references. For true multi-agent scenarios, use the `@utils.escalate` or connection-based handoff patterns.\n- **Open Questions**: Will SOMA node resolution be fixed in a future planner update?\n\n---\n\n### Issue 14: Previously valid OpenAPI schemas now fail validation\n- **Status**: OPEN\n- **Date Discovered**: 2026-02-14\n- **Affects**: External Service actions using OpenAPI 3.0 schemas\n- **Symptom**: OpenAPI schemas that previously passed validation and worked with `externalService://` targets now fail with schema validation errors after org upgrades. No changes were made to the schema files.\n- **Root Cause**: Salesforce tightened OpenAPI schema validation rules in recent releases. Schemas that were previously accepted with minor deviations (e.g., missing `info.version`, non-standard extensions) are now rejected.\n- **Workaround**: Re-validate schemas against strict OpenAPI 3.0 spec. Common fixes: ensure `info.version` is present, remove non-standard `x-` extensions, verify all `$ref` paths resolve correctly.\n- **Open Questions**: Will Salesforce publish the exact validation rules that changed?\n\n---\n\n### Issue 15: Action definitions without `outputs:` block cause \"Internal Error\" on publish\n- **Status**: WORKAROUND\n- **Date Discovered**: 2026-02-16\n- **Date Updated**: 2026-02-17 (TDD v2.1.0 — clarified outputs specifically required)\n- **Affects**: `sf agent publish authoring-bundle` with topic-level action definitions\n- **Symptom**: `sf agent publish` returns \"Internal Error, try again later\" when topic-level action definitions have `target:` but no `outputs:` block. Also triggered when using `inputs:` without `outputs:`. LSP + CLI validation both PASS — error is server-side compilation only.\n- **Root Cause**: The server-side compiler needs output type contracts to resolve `flow://` and `apex://` action targets. Without an `outputs:` block, the compiler cannot generate return bindings. The `inputs:` block alone is NOT sufficient — `outputs:` is specifically required.\n- **Workaround**: Always include an `outputs:` block in action definitions. The `inputs:` block can be omitted if the target has no required inputs (the LLM will still slot-fill via `with param=...`), but `outputs:` must always be present.\n- **TDD Validation**: `Val_No_Outputs` (v2.1.0) confirms inputs-only action definition → \"Internal Error\". `Val_Partial_Output` confirms declaring a subset of outputs IS valid. `Val_Apex_Bare_Output` confirms bare `@InvocableMethod` without wrapper classes also triggers this error.\n- **Cross-reference**: See also Issue 27 — a related but distinct error occurs when action I/O is present but doesn't match ALL invocable target parameters.\n- **Open Questions**: Will the compiler be updated to infer I/O schemas from the target's metadata?\n\n---\n\n### Issue 17: `EinsteinAgentApiChannel` surfaceType not available on all orgs\n- **Status**: OPEN\n- **Date Discovered**: 2026-02-16\n- **Affects**: Agent Runtime API channel enablement via `plannerSurfaces` metadata\n- **Symptom**: Adding `plannerSurfaces` with `surfaceType: EinsteinAgentApiChannel` causes deployment errors on some orgs. Valid surfaceType values on tested orgs: `Messaging`, `CustomerWebClient`, `Telephony`, `NextGenChat`.\n- **Root Cause**: The `EinsteinAgentApiChannel` surfaceType may require specific org features or licenses that are not universally available.\n- **Workaround**: Use `CustomerWebClient` for Agent Runtime API / CLI testing. This surfaceType is available on all tested orgs and enables API access.\n- **Open Questions**: Is `EinsteinAgentApiChannel` limited to specific editions or feature flags?\n\n---\n\n### Issue 18: `connection messaging:` only generates `Messaging` plannerSurface — `CustomerWebClient` dropped on every publish\n- **Status**: OPEN\n- **Date Discovered**: 2026-02-17\n- **Affects**: Agent Builder Preview, Agent Runtime API testing, CLI testing (`sf agent test`, `sf agent preview`)\n- **Symptom**: After `sf agent publish authoring-bundle`, the compiled GenAiPlannerBundle only contains a `Messaging` plannerSurface. `CustomerWebClient` is never auto-generated. Agent Builder Preview shows \"Something went wrong. Refresh and try again.\" because it requires `CustomerWebClient`.\n- **Root Cause**: The `connection messaging:` DSL block only generates a `Messaging` plannerSurface during compilation. There is no `connection customerwebclient:` DSL syntax — attempting it causes `ERROR_HTTP_404` on publish. The compiler has no mechanism to auto-generate `CustomerWebClient`.\n- **Impact**: Every publish overwrites the GenAiPlannerBundle, dropping any manually-added `CustomerWebClient` surface. This requires a post-publish patch after EVERY publish.\n- **Workaround — 6-Step Post-Publish Patch Workflow:**\n 1. `sf agent publish authoring-bundle --api-name AgentName -o TARGET_ORG --json` → creates new version (e.g., v22)\n 2. `sf project retrieve start --metadata \"GenAiPlannerBundle:AgentName_vNN\" -o TARGET_ORG --json` → retrieve compiled bundle\n 3. Manually add second `\u003cplannerSurfaces>` block to the XML with `\u003csurfaceType>CustomerWebClient\u003c/surfaceType>` (copy the existing `Messaging` block, change surfaceType and surface fields)\n 4. `sf agent deactivate --api-name AgentName -o TARGET_ORG` → deactivate agent (deploy fails while active)\n 5. `sf project deploy start --metadata \"GenAiPlannerBundle:AgentName_vNN\" -o TARGET_ORG --json` → deploy patched bundle\n 6. `sf agent activate --api-name AgentName -o TARGET_ORG` → reactivate agent\n- **Patch XML Example:**\n ```xml\n \u003c!-- Add this AFTER the existing Messaging plannerSurfaces block -->\n \u003cplannerSurfaces>\n \u003cadaptiveResponseAllowed>false\u003c/adaptiveResponseAllowed>\n \u003ccallRecordingAllowed>false\u003c/callRecordingAllowed>\n \u003coutboundRouteConfigs>\n \u003cescalationMessage>One moment while I connect you with a support specialist.\u003c/escalationMessage>\n \u003coutboundRouteName>Route_from_Your_Agent\u003c/outboundRouteName>\n \u003coutboundRouteType>OmniChannelFlow\u003c/outboundRouteType>\n \u003c/outboundRouteConfigs>\n \u003csurface>SurfaceAction__CustomerWebClient\u003c/surface>\n \u003csurfaceType>CustomerWebClient\u003c/surfaceType>\n \u003c/plannerSurfaces>\n ```\n- **Note**: The `outboundRouteConfigs` should mirror the Messaging surface config. If no routing is configured, omit `outboundRouteConfigs`.\n- **Note**: `outboundRouteName` in compiled XML does NOT need `flow://` prefix — the publisher strips it during compilation. Both forms work in production XML.\n- **Validated on**: Production org, 2026-02-17\n\n---\n\n### Issue 19: Comments inside `if` blocks treated as empty body\n- **Status**: OPEN\n- **Date Discovered**: 2026-03-04\n- **Affects**: `if`/`else` blocks in `instructions: ->`\n- **Symptom**: An `if` block containing only comments (e.g., `# TODO`) compiles but produces an empty body at runtime. The parser strips comments during tokenization, and the resulting `INDENT → DEDENT` with no executable statements creates a no-op branch that silently swallows the conditional path.\n- **Root Cause**: Comments are not executable statements in Agent Script. The parser treats a comment-only block identically to an empty block.\n- **Workaround**: Always include at least one executable statement (`| text`, `run`, `set`, or `transition`) in every `if`/`else` block. Never use comment-only blocks as placeholders.\n ```yaml\n # ❌ WRONG — empty body after comment stripping\n if @variables.premium == True:\n # TODO: add premium greeting\n\n # ✅ CORRECT — executable statement present\n if @variables.premium == True:\n | Welcome back, valued premium member!\n ```\n- **Open Questions**: Will the compiler emit a warning for empty `if` bodies?\n\n---\n\n### Issue 20: GenAiPlannerBundle / AiAuthoringBundle / GenAiFunction NOT SOQL-queryable\n- **Status**: WORKAROUND (by design — metadata types, not sObjects)\n- **Date Discovered**: 2026-03-04\n- **Affects**: Any workflow that attempts SOQL queries on agent metadata types\n- **Symptom**: `SELECT ... FROM GenAiPlannerBundle` returns `INVALID_TYPE: GenAiPlannerBundle`. Same for `AiAuthoringBundle` and `GenAiFunction`. These types do not appear in `EntityDefinition` SOQL queries.\n- **Root Cause**: These are **Metadata API types**, not sObjects. They exist in the metadata layer and are only accessible via `sf project retrieve start --metadata` or the Metadata API. This is by design, not a bug.\n- **Workaround**: Use `sf project retrieve start --metadata \"TypeName:ApiName\"` instead of SOQL. For querying agent status/versions via SOQL, use `BotDefinition` and `BotVersion` sObjects.\n ```bash\n # ❌ WRONG — these are NOT sObjects\n sf data query --query \"SELECT Id FROM GenAiPlannerBundle\" -o ORG --json\n sf data query --query \"SELECT Id FROM AiAuthoringBundle\" -o ORG --json\n\n # ✅ CORRECT — use Metadata API\n sf project retrieve start --metadata \"GenAiPlannerBundle:MyAgent_v1\" -o ORG --json\n sf project retrieve start --metadata \"AiAuthoringBundle:MyAgent\" -o ORG --json\n\n # ✅ CORRECT — query sObjects for agent info\n sf data query --query \"SELECT Id, DeveloperName FROM BotDefinition WHERE DeveloperName = 'MyAgent'\" -o ORG --json\n sf data query --query \"SELECT Id, VersionNumber, Status FROM BotVersion WHERE BotDefinition.DeveloperName = 'MyAgent'\" -o ORG --json\n ```\n- **Open Questions**: None — this is by design.\n\n---\n\n### Issue 21: Old GenAiPlannerBundle versions block Apex class deletion\n- **Status**: WORKAROUND\n- **Date Discovered**: 2026-03-10\n- **Affects**: Apex class lifecycle when iterating agent versions\n- **Symptom**: `sf project delete source --metadata ApexClass:MyClass` fails with \"setup object in use\" even though the Apex class is only referenced by inactive/old GenAiPlannerBundle versions. The current active bundle may reference a different (newer) Apex class, but old bundles still hold GenAiFunction references to the original.\n- **Root Cause**: Each `sf agent publish authoring-bundle` creates a new GenAiPlannerBundle version. Old versions remain in the org and retain `GenAiPluginActionDefinition` records that reference the Apex class via `GenAiFunction`. The platform blocks deletion of any metadata referenced by ANY GenAiFunction — active or not.\n- **Workaround**: Delete old bundles using the Orphan-Then-Delete pattern (Tooling API PATCH to null `PlannerId` on child `GenAiPluginDefinition` records, then DELETE the parent `GenAiPlannerBundle`). After all referring bundles are removed, the Apex class can be deleted normally.\n - **Note**: Apex test classes (not referenced by GenAiFunction) can be deleted freely at any time.\n- **Open Questions**: Will Salesforce add cascade cleanup for inactive bundle versions?\n- **References**: See [metadata-lifecycle.md](metadata-lifecycle.md) for the full Orphan-Then-Delete pattern and API comparison table.\n\n---\n\n### Issue 22: Flow version drift causes publish failure\n- **Status**: WORKAROUND\n- **Date Discovered**: 2026-03-10\n- **Affects**: Action definitions targeting `flow://` with I/O schemas\n- **Symptom**: `sf agent publish authoring-bundle` fails with I/O mismatch errors when the action's `inputs:` / `outputs:` block matches the latest Flow version but NOT the active Flow version. The publisher validates against the **active** version's I/O contract.\n- **Root Cause**: `sf project retrieve start --metadata Flow:FlowName` retrieves the **latest** Flow version, which may be Draft or Obsolete. If the latest version renamed, added, or removed inputs/outputs compared to the active version, the locally-defined action I/O will not match what the publisher expects.\n- **Example**: Flow `Locate_Items` has v2 (active) with input `IncomingCaseId` and v5 (latest, Obsolete) with input `RecordId`. Retrieving the Flow gives v5 metadata, so the developer defines `inputs: record_id: string` — but publish validates against v2's `IncomingCaseId`.\n- **Workaround**: Before defining action I/O, verify the active Flow version's inputs:\n ```bash\n sf data query --query \"SELECT ActiveVersionId, LatestVersionId FROM FlowDefinitionView WHERE ApiName = 'My_Flow' LIMIT 1\" -o TARGET_ORG --json\n ```\n If `ActiveVersionId != LatestVersionId`, inspect the active version's I/O before writing your action definition.\n- **Open Questions**: Will `sf project retrieve start` gain a `--active-version` flag for Flows?\n- **References**: See [metadata-lifecycle.md](metadata-lifecycle.md) § \"Flow Version Drift\" and [cli-guide.md](cli-guide.md) § \"Step 1: Retrieve\" warning box.\n\n---\n\n### Issue 23: HTTP 500 on publish when `default_agent_user` is set for Employee Agent\n- **Status**: WORKAROUND\n- **Date Discovered**: 2026-03-11\n- **Affects**: `sf agent publish authoring-bundle`\n- **Symptom**: Publish fails with generic HTTP 500 error (no detailed message). Occurs when `default_agent_user` is included in the config block for an `AgentforceEmployeeAgent`, or when `default_agent_user` references a non-existent user for a Service Agent.\n- **Root Cause**: The Einstein Platform tries to resolve the `default_agent_user` reference during publish. For Employee Agents, this field is invalid entirely. For Service Agents, if the user doesn't exist or lacks the Einstein Agent User profile, the API returns 500 instead of a descriptive error.\n- **Workaround**: For Employee Agents, remove `default_agent_user` entirely and set `agent_type: \"AgentforceEmployeeAgent\"`. For Service Agents, verify the user exists and is truly valid for publish: active, not `AutomatedProcess`, and `Profile.Name = 'Einstein Agent User'`.\n- **Extra diagnostic**: If `sf agent publish authoring-bundle --json` still fails after validate/preview pass, retry with `--skip-retrieve`. If `--skip-retrieve` succeeds, the failure was in the CLI retrieve/deploy-back phase rather than in the publish itself.\n- **Open Questions**: Will the API return a descriptive error instead of 500? Will the CLI expose retrieve-phase failures separately from publish failures?\n\n---\n\n### Issue 24: Omitting `agent_type` silently defaults to Service Agent behavior\n- **Status**: WORKAROUND\n- **Date Discovered**: 2026-03-11\n- **Affects**: Agent config block\n- **Symptom**: Agent without explicit `agent_type` behaves as Service Agent. If `default_agent_user` is also missing, publish fails with HTTP 500. No warning or error is emitted about the missing field.\n- **Root Cause**: The compiler defaults to `EinsteinServiceAgent` internally when `agent_type` is not specified.\n- **Workaround**: Always set `agent_type` explicitly to `\"AgentforceServiceAgent\"` or `\"AgentforceEmployeeAgent\"`.\n- **Open Questions**: Will the compiler emit a warning when `agent_type` is omitted?\n\n---\n\n### Issue 25: GenAiPluginDefinition Duplicate Name Collision\n\n**Symptom**: Publish fails with `GenAiPluginDefinition` name conflict error.\n\n**Cause**: When `start_agent` and a `topic` share the same API name, the generated GenAiPluginDefinition metadata collides — both produce identically-named plugin definitions.\n\n**Workaround**: Ensure `start_agent` and all `topic` blocks use unique API names. A common pattern is to prefix the start agent name (e.g., `Start_My_Agent` vs `My_Agent_Topic`).\n\n---\n\n### Issue 26: `is_required: True` is a no-op for the planner\n- **Status**: OPEN\n- **Date Discovered**: 2026-03-11\n- **Affects**: Action input definitions with `is_required: True`\n- **Symptom**: Actions are invoked by the planner with empty or missing values for inputs marked `is_required: True`. The planner does not enforce the required constraint — it treats it as a hint, not a hard gate. Users expect the planner to refuse invocation until all required inputs are filled, but it proceeds regardless.\n- **Root Cause**: The `is_required` property compiles and is stored in the GenAiFunction metadata, but the planner does not validate required inputs before action invocation. The property only influences LLM slot-filling behavior (the LLM _tries_ to collect the value but is not blocked from proceeding without it).\n- **Workaround**: Use `available when` guards to enforce required inputs deterministically:\n ```yaml\n # ❌ INSUFFICIENT — planner ignores is_required\n inputs:\n order_id: string\n is_required: True\n\n # ✅ CORRECT — deterministic enforcement\n reasoning:\n actions:\n lookup: @actions.get_order\n available when @variables.order_id is not None\n ```\n- **Open Questions**: Will the planner enforce `is_required` in a future release?\n- **Cross-reference**: See syntax-reference.md § Action Metadata Properties, Common Pitfalls.\n\n---\n\n### Issue 27: ConcurrentModificationException when action I/O doesn't match invocable target\n- **Status**: WORKAROUND (fix expected in 260.4)\n- **Date Discovered**: 2026-03-11\n- **Affects**: Action definitions with `inputs:` / `outputs:` blocks where the I/O schema does not declare ALL parameters on the invocable target\n- **Symptom**: `sf agent publish authoring-bundle` fails with `ConcurrentModificationException` or \"Internal Error\" when the action definition's I/O schema is a subset of the target's invocable parameters. For example, if a Flow has 5 inputs but the action only declares 3, the server-side compiler throws an internal error.\n- **Root Cause**: The compiler iterates over target parameters and agent I/O simultaneously. When the agent-side schema omits parameters that exist on the target, the iterator state becomes inconsistent, causing a `ConcurrentModificationException`.\n- **Workaround**: Declare ALL inputs and outputs from the invocable target in the action definition, even parameters the agent doesn't use. Unused inputs can be given a default or left for the LLM to fill. Unused outputs can be marked `filter_from_agent: True`.\n ```yaml\n # ❌ WRONG — subset of target params causes ConcurrentModificationException\n inputs:\n customer_id: string\n # (target also has order_type, priority — not declared)\n\n # ✅ CORRECT — declare all target params\n inputs:\n customer_id: string\n order_type: string\n description: \"Type of order\"\n priority: string\n description: \"Priority level\"\n ```\n- **Open Questions**: Fix expected in release 260.4. Will partial I/O schemas be supported?\n- **Cross-reference**: Related to Issue 15 (missing outputs block). Issue 15 covers the case where `outputs:` is absent entirely; this issue covers the case where I/O is present but incomplete.\n\n---\n\n\u003ca id=\"issue-28\">\u003c/a>\n\n### Issue 28: `date` primitive type in action I/O causes runtime error\n- **Status**: WORKAROUND\n- **Date Discovered**: 2026-03-11\n- **Affects**: Action input/output definitions using `date` as the type\n- **Symptom**: Actions with `date` typed inputs or outputs compile and publish without error, but fail at runtime with `'Date'` type error when the action is invoked. The LLM-provided value cannot be coerced into the platform's expected Date format.\n- **Root Cause**: The `date` primitive in Agent Script compiles to a type that the action invocation runtime does not handle correctly. The runtime expects a specific ISO-8601 format that the LLM's slot-filled value does not match.\n- **Workaround**: Use `object` type with `complex_data_type_name` referencing the Lightning date type:\n ```yaml\n # ❌ WRONG — causes runtime error 'Date'\n inputs:\n appointment_date: date\n\n # ✅ CORRECT — use object + Lightning type mapping\n inputs:\n appointment_date: object\n complex_data_type_name: \"lightning__dateType\"\n ```\n- **Open Questions**: Will the `date` primitive be fixed to work in action I/O?\n- **Cross-reference**: See syntax-reference.md § Variable Types, complex-data-types.md.\n\n---\n\n### Issue 29: ANTLR parser error for large/complex .agent files\n- **Status**: OPEN\n- **Date Discovered**: 2026-03-11\n- **Affects**: Large `.agent` files (approximately 1,664+ lines observed)\n- **Symptom**: The Agent Script parser (ANTLR-based) fails with a parser error when processing very large or highly complex `.agent` files. The error is non-specific and occurs during the parsing phase before any semantic validation.\n- **Root Cause**: The ANTLR parser has practical limits on grammar complexity and token count. Very large agents with many topics, deep nesting, or extensive instruction blocks can exceed these limits.\n- **Workaround**: None confirmed. Potential mitigations:\n 1. Reduce agent size by simplifying instructions (move verbose prompts into Prompt Templates)\n 2. Reduce topic count by consolidating related topics\n 3. Use topic delegation (`@topic.X`) to split into multiple smaller agents\n- **Open Questions**: What is the exact line/token limit? Will the parser be updated to handle larger files?\n\n---\n\n### Issue 30: 500 \"Failed to create planner session\" (widespread, multi-version)\n- **Status**: OPEN\n- **Date Discovered**: 2026-03-11\n- **Affects**: Agent runtime — all agent types and versions\n- **Symptom**: Agent returns HTTP 500 with \"Failed to create planner session\" when a user initiates a conversation. Error is intermittent and affects multiple orgs across different Salesforce releases. Not correlated with agent complexity, user permissions, or time of day.\n- **Root Cause**: Unknown. The error originates in the planner session initialization layer. Suspected to be a transient infrastructure issue on the Einstein Platform side.\n- **Workaround**: None reliable. Retry after a few minutes. If persistent:\n 1. Deactivate and reactivate the agent\n 2. Re-publish the authoring bundle (creates a new BotVersion)\n 3. Contact Salesforce support with the session ID and timestamp\n- **Open Questions**: Is this a known infrastructure issue with an ETA for fix?\n\n---\n\n\u003ca id=\"issue-31\">\u003c/a>\n\n### Issue 31: Escalation loops when no human agents available\n- **Status**: WORKAROUND\n- **Date Discovered**: 2026-03-11\n- **Affects**: `@utils.escalate` action in agents deployed without live human agents\n- **Symptom**: When `@utils.escalate` is invoked but no human agents are available (e.g., outside business hours, no Omni-Channel agents online), the escalation fails silently and the conversation re-enters the same topic. This triggers the escalation logic again, creating an infinite loop. The built-in 3-4 loop guardrail eventually breaks the cycle, but the user receives confusing repeated messages.\n- **Root Cause**: `@utils.escalate` does not return a success/failure status. There is no built-in mechanism to detect whether human agents are available before attempting escalation, and no way to handle a failed escalation gracefully.\n- **Workaround**: Check agent availability before escalating, and provide a fallback path:\n ```yaml\n variables:\n escalation_attempted: mutable boolean = False\n\n topic escalation:\n description: \"Escalate to human agent\"\n reasoning:\n instructions: ->\n if @variables.escalation_attempted == True:\n | I'm sorry, but our team is currently unavailable.\n | Please try again during business hours or leave a message.\n transition to @topic.leave_message\n | Let me connect you with a support specialist.\n actions:\n handoff: @utils.escalate\n description: \"Transfer to human agent\"\n\n before_reasoning:\n if @variables.escalation_attempted == True:\n # Already tried — don't loop\n transition to @topic.leave_message\n set @variables.escalation_attempted = True\n ```\n- **Open Questions**: Will `@utils.escalate` return a status code in a future release? Will a built-in availability check action be added?\n- **Cross-reference**: See fsm-architecture.md § Pattern 5 (HANDOFF) — Escalation with Availability Check sub-pattern. See production-gotchas.md § Escalation Fallback Loop.\n\n---\n\n### Issue 32: Citations not rendering in NGA agents vs legacy agents\n- **Status**: OPEN\n- **Date Discovered**: 2026-03-11\n- **Affects**: Next-Generation Agent (NGA / Agent Script) agents using knowledge bases with citations enabled\n- **Symptom**: Despite enabling all citation settings (`citations_enabled: True`, `citations_url` configured, knowledge base attached), citations do not render in agent responses for NGA agents. The same knowledge base with citations works correctly in legacy Einstein Bot agents.\n- **Root Cause**: The citation rendering pipeline is not fully implemented for the NGA/Agent Script agent runtime. Legacy agents use a different response rendering path that supports citation markup.\n- **Workaround**: None for NGA agents. If citations are critical, use a legacy agent for knowledge-intensive topics, or include source URLs directly in the knowledge article content so they appear in the response text.\n- **Open Questions**: Will citation rendering be added to the NGA runtime? Is there a timeline?\n\n---\n\n### Issue 33: `== []` and `set ... = []` — empty list literal not supported in expressions\n- **Status**: WORKAROUND\n- **Date Discovered**: 2026-03-12\n- **Affects**: Expressions using `[]` literal for empty list comparison or assignment\n- **Symptom**: Two related parse errors:\n 1. `if @variables.my_list == []:` → parse error (`[` not allowed in expression position)\n 2. `set @variables.my_list = []` → parse error (`[` not allowed in assignment position)\n- **Root Cause**: The expression parser's sandboxed Python AST subset does not include list literal construction. `[]` is valid in `mutable list[T] = []` declarations (handled by the declaration parser), but NOT in runtime expressions.\n- **Workaround**:\n - Empty check: `if @variables.my_list is None or len(@variables.my_list) == 0:`\n - Reset to empty: Use a dedicated empty list variable and copy:\n ```yaml\n variables:\n empty_temp: mutable list[string] = []\n my_list: mutable list[string] = []\n\n # To reset my_list at runtime:\n set @variables.my_list = @variables.empty_temp\n ```\n- **Open Questions**: Will `[]` be added to the expression parser?\n- **Cross-reference**: See syntax-reference.md § Expression Limitations.\n\n---\n\n\u003ca id=\"issue-34\">\u003c/a>\n\n### Issue 34: `is_displayable: True` on prompt template outputs causes blank response\n- **Status**: WORKAROUND\n- **Date Discovered**: 2026-03-12\n- **Affects**: Action outputs on prompt template actions (`prompt://` / `generatePromptResponse://` targets)\n- **Symptom**: Setting `is_displayable: True` (or toggling \"Show in conversation\" in the UI) on a prompt template action's output causes the agent to return a blank or empty response. The prompt template executes correctly (visible in trace), but the response text is not surfaced to the user.\n- **Root Cause**: When `is_displayable: True`, the platform attempts to render the raw prompt template output directly instead of letting the reasoner synthesize it. The rendering pipeline does not handle prompt template output format correctly, resulting in blank display.\n- **Workaround**: Set `is_displayable: False` on prompt template outputs and let the reasoner synthesize the output into its response naturally. If the output should influence the reply or routing, also set `is_used_by_planner: True` on that field.\n ```yaml\n # ❌ WRONG — causes blank response\n outputs:\n response_text: string\n is_displayable: True\n\n # ✅ CORRECT — reasoner synthesizes output\n outputs:\n response_text: string\n is_displayable: False\n is_used_by_planner: True\n ```\n- **Open Questions**: Will the rendering pipeline be updated to handle prompt template output with `is_displayable: True`?\n- **Cross-reference**: See production-gotchas.md § `is_displayable: True` on Prompt Outputs.\n\n---\n\n### Issue 35: Topic `description:` with line breaks breaks the script\n- **Status**: WORKAROUND\n- **Date Discovered**: 2026-03-12\n- **Affects**: `topic` and `start_agent` blocks with multi-line `description:` values\n- **Symptom**: Including line breaks (literal newlines) inside a topic's `description: \"...\"` string causes the script to fail during parsing. The error is non-specific and may manifest as a syntax error on the following line.\n- **Root Cause**: The topic `description:` parser expects a single-line string value. Unlike `instructions:` which supports `|` and `->` multiline syntax, `description:` does not have a multiline variant.\n- **Workaround**: Keep topic descriptions on a single line. Remove all line breaks.\n ```yaml\n # ❌ WRONG — line break in description\n topic support:\n description: \"Handles customer support requests\n including billing and technical issues\"\n\n # ✅ CORRECT — single line\n topic support:\n description: \"Handles customer support requests including billing and technical issues\"\n ```\n- **Open Questions**: Will `description:` gain multiline support (e.g., `description: |`)?\n\n---\n\n### Issue 36: Variable names conflict with system context variable mappings\n- **Status**: WORKAROUND\n- **Date Discovered**: 2026-03-12\n- **Affects**: Variable declarations using names that match platform context variable field names\n- **Symptom**: Declaring a variable with a name like `Locale` that matches an existing system context variable field name produces the error: \"The field is already mapped to a Context Variable.\" The agent fails to compile.\n- **Root Cause**: The platform reserves certain variable names for internal context variable mappings (e.g., `MessagingSession` fields like `Locale`, `Channel`, `Status`). These names collide with user-declared variables at the namespace level.\n- **Workaround**: Use unique, prefixed names that avoid common MessagingSession and system field names:\n ```yaml\n # ❌ WRONG — collides with system context mapping\n variables:\n Locale: mutable string = \"\"\n\n # ✅ CORRECT — unique prefixed name\n variables:\n customer_locale: mutable string = \"\"\n ```\n **Known reserved names** (partial list): `Locale`, `Channel`, `Status`, `Origin`\n- **Open Questions**: Will Salesforce publish a complete list of reserved context variable names?\n\n---\n\n### Issue 37: Draft version works but Committed version fails\n- **Status**: OPEN\n- **Date Discovered**: 2026-03-12\n- **Affects**: Agent behavior difference between Draft and Committed/Active versions\n- **Symptom**: Actions, variable updates, and topic routing that work correctly in Draft mode (preview) fail silently or produce different behavior in the Committed/Active version. The agent may skip actions, fail to update variables, or route incorrectly.\n- **Root Cause**: Multiple potential causes reported: graph serialization differences between Draft and Committed compilation, action registry timing (Draft resolves actions lazily, Committed resolves eagerly), and stale cache from previous versions.\n- **Workaround**: No universal workaround. Mitigation strategies:\n 1. Re-commit (deactivate → re-publish → re-activate) to force a clean compilation\n 2. Verify all action targets exist and are active in the org\n 3. Compare Draft and Committed trace outputs to identify specific divergence points\n- **Open Questions**: Will the Draft and Committed compilation paths be unified?\n\n---\n\n### Issue 38: Object variables not editable in preview\n- **Status**: OPEN\n- **Date Discovered**: 2026-03-12\n- **Affects**: `sf agent preview` and Agent Builder Preview\n- **Symptom**: Only scalar variable types (`string`, `number`, `boolean`) can be overridden in the preview interface. Object and list variables cannot be edited or injected — the UI does not provide input fields for complex types.\n- **Root Cause**: The preview interface only renders scalar input controls. No JSON editor or structured input is provided for `object` or `list[T]` types.\n- **Workaround**: For values needed during testing:\n 1. Use scalar variables that can be set in preview, then assemble in logic\n 2. Test object/list variable scenarios via the Agent Runtime API (Connect API) which accepts JSON payloads\n 3. Pre-populate object variables in `before_reasoning:` for testing\n- **Open Questions**: Will the preview interface add JSON editing for object/list variables?\n\n---\n\n\u003ca id=\"issue-39\">\u003c/a>\n\n### Issue 39: `@context.*` linked sources not supported for NGA Service Agents\n- **Status**: WORKAROUND\n- **Date Discovered**: 2026-03-12\n- **Affects**: Linked variables using `@context.*` sources in NGA Service Agents (ExternalCopilot / EinsteinServiceAgent)\n- **Symptom**: Linked variables with `source: @context.currentRecordId`, `source: @context.customerId`, or other `@context.*` references produce \"Unsupported data type\" errors at runtime in NGA Service Agents. The variables compile and publish without error but fail when the agent starts a conversation.\n- **Root Cause**: `@context.*` references resolve **page-level context** from Lightning Experience (LEX). This context is only available to **Employee Agents** embedded on LEX record pages where a `recordId` and user context are present. NGA Service Agents (chat, messaging) do not have LEX page context — they receive channel context via `@MessagingSession.*` instead.\n- **Workaround**: For Service Agents, use `@MessagingSession.*` for channel context:\n ```yaml\n # ❌ WRONG for Service Agents — @context not available\n variables:\n customer_id: linked string\n source: @context.customerId\n\n # ✅ CORRECT for Service Agents — use messaging channel context\n variables:\n end_user_id: linked string\n source: @MessagingSession.MessagingEndUserId\n session_key: linked string\n source: @MessagingSession.MessagingSessionKey\n\n # ✅ CORRECT for Employee Agents on LEX — @context IS available\n variables:\n record_id: linked string\n source: @context.currentRecordId\n ```\n- **Open Questions**: Will `@context.*` be extended to Service Agent channels?\n- **Cross-reference**: See syntax-reference.md § Resource References, Common Linked Variable Sources.\n\n---\n\n\u003ca id=\"issue-40\">\u003c/a>\n\n### Issue 40: Draft prompt template causes misleading publish errors for `generatePromptResponse://` actions\n- **Status**: WORKAROUND\n- **Date Discovered**: 2026-03-17\n- **Affects**: `sf agent publish authoring-bundle` with `generatePromptResponse://` action targets\n- **Symptom**: Publish fails with `invalid input parameters found: 'Input:query'` or `invalid output parameters found: 'promptResponse'` — implying the I/O names are wrong. Removing I/O blocks produces `Metadata API request failed: Metadata retrieval failed:` or `Internal Error, try again later`. `sf agent validate authoring-bundle` passes in all cases.\n- **Root Cause**: The target prompt template is in **Draft** status. The platform cannot resolve the `generatePromptResponse://` target, but reports the failure as invalid I/O parameters or generic internal errors rather than indicating the template status.\n- **Workaround**: Activate the prompt template in **Setup > Prompt Builder** before publishing. The original Agent Script syntax (with `\"Input:X\"` inputs and `promptResponse` output) publishes successfully once the template is Active. Pre-publish check: retrieve the template XML and verify `\u003cstatus>Published\u003c/status>` or `\u003cstatus>Active\u003c/status>`.\n- **Open Questions**: Will Salesforce improve the error message to mention template status? Will `sf agent validate` add a target resolution check?\n- **References**: [action-prompt-templates.md](action-prompt-templates.md#draft-template-publish-errors) — \"Draft Template Publish Errors\" section\n\n---\n\n## Resolved Issues\n\n\u003ca id=\"issue-16\">\u003c/a>\n\n### Issue 16: `connections:` (plural) wrapper block not valid — use `connection messaging:` (singular)\n- **Status**: RESOLVED\n- **Date Discovered**: 2026-02-16\n- **Date Resolved**: 2026-02-16\n- **Affects**: Agent Script escalation routing configuration\n- **Symptom**: CLI validation rejects `connections:` (plural wrapper) block with `SyntaxError: Invalid syntax after conditional statement`.\n- **Root Cause**: The correct syntax is `connection messaging:` (singular, standalone top-level block) — NOT the `connections:` plural wrapper shown in some docs and `future_recipes/`. The `connection \u003cchannel>:` block is a Beta Feature available on production orgs.\n- **Resolution**: Use `connection messaging:` as a standalone block (no wrapper). Both minimal form (`adaptive_response_allowed` only) and full form (with `outbound_route_type`, `outbound_route_name`, `escalation_message`) are validated.\n- **CRITICAL**: `outbound_route_name` requires `flow://` prefix — bare API name causes `ERROR_HTTP_404` on publish. Correct format: `\"flow://My_Flow_Name\"`.\n- **All-or-nothing rule**: When `outbound_route_type` is present, all three route properties are required.\n- **Note**: `outboundRouteName` in compiled XML does NOT need the `flow://` prefix — the publisher strips it during compilation. Both forms work in production XML.\n- **Validated on**: Production org, 2026-02-16\n\n\u003ca id=\"issue-40-filter-planner-conflict\">\u003c/a>\n\n### Issue 40: `filter_from_agent` + `is_used_by_planner` on same output — `InvalidFormatError` with cascade\n\n- **Status**: WORKAROUND\n- **Severity**: High (Blocking)\n- **Discovered**: 2026-03-17\n- **Affects**: All Agent Script action output field declarations\n- **Symptom**: Output fields that declare both `filter_from_agent` and `is_used_by_planner` produce `InvalidFormatError` with the message: _\"Remove the 'is_used_by_planner' field and use only 'filter_from_agent'.\"_ More critically, this **invalidates the entire action definition**, causing cascading `ACTION_NOT_IN_SCOPE` errors everywhere the action is referenced — in `before_reasoning:` `run @actions.X` calls and `reasoning.actions:` invocations. The cascade makes the root cause very hard to diagnose because the error appears on action _references_, not on the offending output field.\n- **Root Cause**: `filter_from_agent` and `is_used_by_planner` are mutually exclusive output visibility controls. When both are present, the Agent Script parser rejects the output field, which poisons the entire action definition and makes it invisible to all scopes.\n- **Cascade Pattern**: One invalid output field on a shared action (e.g., `update_session` reused across topics) can trigger `ACTION_NOT_IN_SCOPE` errors in every topic that references it — often 4-6+ errors from a single root cause.\n- **Workaround**: Use only `filter_from_agent: True` on outputs that should be hidden from the customer. Remove `is_used_by_planner` from those fields entirely. The planner can still reason about the output via `set @variables.X = @outputs.Y` bindings in reasoning actions.\n- **Example**:\n ```yaml\n # ❌ WRONG — causes InvalidFormatError + cascade\n outputs:\n caseId: string\n filter_from_agent: True\n is_used_by_planner: False\n\n # ✅ CORRECT — filter_from_agent alone\n outputs:\n caseId: string\n filter_from_agent: True\n ```\n- **Validator Rule**: `ASV-RUN-020` (Blocking)\n- **Real-world impact**: Observed in a production agent — 6 output fields across 5 topics caused 4 cascading `ACTION_NOT_IN_SCOPE` errors that blocked publish.\n- **Validated on**: Production sandbox, 2026-03-17\n\n### Issue 41: Lifecycle arithmetic on mutable number crashes when variable is `None` at runtime\n\n- **Status**: WORKAROUND\n- **Severity**: High (Silent crash)\n- **Discovered**: 2026-03-17\n- **Affects**: `before_reasoning` / `after_reasoning` blocks with arithmetic on mutable number variables\n- **Symptom**: Agent crashes silently with \"unexpected error\" when a `before_reasoning` block does `set @variables.counter = @variables.counter + 1` and the variable is `None` at runtime. The crash is `None + 1` — a type error in the expression evaluator. No useful error message is surfaced to the user or logs.\n- **Root Cause**: Mutable number variables declared with `= 0` defaults can arrive as `None` at runtime. Known triggers: (1) Eval API / Testing Center state injection that omits the variable, (2) platform variable initialization bugs where defaults are not applied before the first `before_reasoning` execution.\n- **Cascade Pattern**: Common in guardrail topics where a strike counter is incremented on every entry. All guardrail topics (e.g., `inappropriate_content`, `prompt_injection`, `reverse_engineering`) crash simultaneously since they share the same counter pattern.\n- **Workaround**: Add a null guard before any arithmetic in lifecycle hooks:\n ```yaml\n # ❌ CRASHES when guardrail_strikes is None\n before_reasoning:\n set @variables.guardrail_strikes = @variables.guardrail_strikes + 1\n if @variables.guardrail_strikes >= 3:\n transition to @topic.escalation\n\n # ✅ SAFE — null guard prevents None + 1\n before_reasoning:\n if @variables.guardrail_strikes is None:\n set @variables.guardrail_strikes = 0\n set @variables.guardrail_strikes = @variables.guardrail_strikes + 1\n if @variables.guardrail_strikes >= 3:\n transition to @topic.escalation\n ```\n- **Validator Rule**: `ASV-RUN-021` (Warning)\n- **Validated on**: Production sandbox, 2026-03-17\n\n### Issue 42: Raw `@system_variables.user_input` substring matching is brittle for deterministic routing\n\n- **Status**: WORKAROUND\n- **Date Discovered**: 2026-03-20\n- **Affects**: Deterministic branching that uses `contains`, `startswith`, or `endswith` directly on `@system_variables.user_input`\n- **Symptom**: Rules such as `if @system_variables.user_input contains \"never mind\":` appear simple, but cancellation/revision detection behaves inconsistently across real user phrasing. The branch may miss intent variants or fail to trigger when punctuation, casing, or phrasing changes.\n- **Root Cause**: `@system_variables.user_input` is raw last-utterance text, not a normalized intent signal. Direct substring checks are too weak for control-flow-critical intent detection.\n- **Workaround**: Normalize the utterance first via Flow, Apex, or a classifier action, then branch on an explicit boolean or enum.\n ```yaml\n # ❌ BRITTLE — raw text matching\n if @system_variables.user_input contains \"never mind\":\n transition to @topic.cancel_request\n\n # ✅ SAFER — normalize first\n run @actions.classify_user_intent\n with utterance = @system_variables.user_input\n set @variables.cancel_requested = @outputs.cancel_requested\n\n if @variables.cancel_requested == True:\n transition to @topic.cancel_request\n ```\n- **Validator Rule**: `ASV-RUN-023` (Warning)\n- **Open Questions**:\n - Will future runtime layers provide stronger deterministic utterance-matching semantics?\n - Will direct raw-text guards become portable enough to recommend for intent routing?\n\n---\n\n## Contributing\n\nWhen you discover a new platform issue during an Agent Script session:\n\n1. Add it to the **Open Issues** section using the template above\n2. Assign the next sequential issue number\n3. Set status to `OPEN` or `WORKAROUND`\n4. Include the date discovered\n5. Be specific about the symptom and any error messages\n6. Note what you've tried so far under \"Workaround\"\n\nWhen an issue is resolved:\n1. Update the status to `RESOLVED`\n2. Add the resolution date and what fixed it (e.g., \"Fixed in Spring '26 release\")\n3. Move the issue to the **Resolved Issues** section\n\n---\n\n*Last updated: 2026-03-20 (v2.9.0)*\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":57220,"content_sha256":"effcc4d5dcdb80f10d7217aa6b9d91cbcf8791d7012892545245c91c21706c58"},{"filename":"references/metadata-lifecycle.md","content":"\u003c!-- Parent: sf-ai-agentscript/SKILL.md -->\n# Agent Metadata Lifecycle & Cleanup\n\n> Versioning, cleanup, and deletion patterns for Agent Script metadata objects.\n\n---\n\n## GenAiPlannerBundle Versioning\n\nEach `sf agent publish authoring-bundle` creates a new GenAiPlannerBundle version. Understanding the naming conventions is critical for cleanup.\n\n### Naming Conventions\n\n| Convention | Example | When Used |\n|-----------|---------|-----------|\n| Hash suffix | `MyAgent_16jXXXXXXXXXXXX` | Default — auto-generated by publisher |\n| Version suffix | `MyAgent_vNN` | Optional — set via GenAiPlannerBundle metadata after publish |\n\nThe hash suffix is non-deterministic — each publish generates a different hash. Over time, an org accumulates many bundle versions (e.g., `_v18`, `_v19`, `_v22`, ..., `_v31`).\n\n### What Each Publish Creates\n\n```\nsf agent publish authoring-bundle\n └── GenAiPlannerBundle (new version)\n ├── GenAiPluginDefinition (per topic)\n ├── GenAiPluginActionDefinition (per action)\n └── plannerSurfaces (channel config)\n```\n\nOld bundle versions remain in the org. They are inactive but retain references to Apex/Flow targets via GenAiFunction definitions.\n\n### Retrieve a Specific BotVersion\n\nWhen you need to inspect or patch a specific published version, retrieve the exact `BotVersion` explicitly:\n\n```bash\nsf project retrieve start \\\n --metadata Bot:MyAgent \\\n --metadata BotVersion:MyAgent.v3 \\\n --target-org TARGET_ORG\n```\n\nCurrent SF CLI releases correctly retrieve the requested `BotVersion` rather than always falling back to only the latest version.\n\n---\n\n## Flow Version Drift\n\n`sf project retrieve start --metadata Flow:FlowName` retrieves the **latest** version (may be Draft/Obsolete), while the publisher validates against the **active** version's I/O.\n\n**Impact**: If the latest Flow version renamed an input (e.g., `RecordId` -> `IncomingCaseId`), your locally-defined action I/O will mismatch the active version at publish time.\n\n**Prevention**: Always verify the active Flow version before defining action I/O:\n\n```bash\nsf data query --query \"SELECT ActiveVersionId, LatestVersionId FROM FlowDefinitionView WHERE ApiName = 'My_Flow' LIMIT 1\" -o TARGET_ORG --json\n```\n\nIf `ActiveVersionId != LatestVersionId`, inspect the active version's inputs via Tooling API before writing your action definition.\n\n---\n\n## Orphan-Then-Delete Pattern\n\nWhen Salesforce returns \"setup object in use\" on metadata deletion, parent-child references create circular dependency locks. The Tooling API can break these references.\n\n### Technique\n\n1. **PATCH child records** to null out their parent reference (e.g., `PlannerId: null`)\n2. **DELETE the now-childless parent** via Tooling API REST DELETE\n3. Repeat up the dependency tree\n\n### Implementation\n\n```bash\n# Step 1: Orphan child PluginDefinitions by nulling PlannerId\n# CRITICAL: Must set unique LocalDeveloperName to avoid DUPLICATE_DEVELOPER_NAME error\ncurl -X PATCH \"$INSTANCE_URL/services/data/vXX.0/tooling/sobjects/GenAiPluginDefinition/PLUGIN_ID\" \\\n -H \"Authorization: Bearer $TOKEN\" \\\n -H \"Content-Type: application/json\" \\\n -d '{\"PlannerId\": null, \"LocalDeveloperName\": \"original_name_del_001\"}'\n\n# Step 2: Delete the now-childless PlannerBundle\ncurl -X DELETE \"$INSTANCE_URL/services/data/vXX.0/tooling/sobjects/GenAiPlannerBundle/BUNDLE_ID\" \\\n -H \"Authorization: Bearer $TOKEN\"\n```\n\n### LocalDeveloperName Uniqueness Constraint\n\nWhen nulling `PlannerId`, the platform enforces `LocalDeveloperName` uniqueness across all orphaned records. Use a unique suffix:\n\n```json\n{\"PlannerId\": null, \"LocalDeveloperName\": \"\u003coriginal_name>_del_\u003csequence>\"}\n```\n\n### API Comparison for Setup Object Deletion\n\n| Method | Result |\n|--------|--------|\n| `sf project delete source` | \"setup object in use\" |\n| SOAP `deleteMetadata` | \"setup object in use\" |\n| Tooling API DELETE (without orphaning) | \"setup object in use\" |\n| REST API DELETE on BotDefinition | \"insufficient access rights\" |\n| Apex DML | \"DML not allowed on [object]\" |\n| Composite API (ordered) | \"setup object in use\" on each subrequest |\n| **Tooling API PATCH + DELETE** | **Works for PlannerBundle hierarchy** |\n\n---\n\n## What Cannot Be Deleted\n\nEven with the Orphan-Then-Delete pattern, certain metadata objects are permanently locked:\n\n| Object | Reason |\n|--------|--------|\n| `ConversationDefinitionDialog` | \"System Dialog\" internal protection flag |\n| `ConversationDefinitionSession` | `deletable: false` at object level |\n| `ConversationDefinitionEventLog` | `deletable: false` at object level |\n| `BotVersion` | Locked by undeletable children above |\n| `BotDefinition` | Locked by BotVersions |\n\n**Practical implication**: Once a `BotDefinition` is created, it cannot be fully deleted via API. The UI \"Delete Agent\" button removes the `AiAuthoringBundle` and `GenAiPlannerBundle`, but NOT the `BotDefinition` itself. Contact Salesforce Support for full cleanup.\n\n---\n\n## Old Bundles Block Apex Deletion\n\nPrevious GenAiPlannerBundle versions retain `GenAiFunction` references to Apex class targets. This creates a dependency chain:\n\n```\nOld GenAiPlannerBundle (v5)\n └── GenAiPluginDefinition\n └── GenAiPluginActionDefinition\n └── References: apex://MyVerificationClass\n```\n\n**Impact**: `sf project delete source --metadata ApexClass:MyVerificationClass` fails with \"setup object in use\" even though the old bundle is inactive.\n\n**Workaround**: Delete old bundles using the Orphan-Then-Delete pattern first, then delete the Apex class. Alternatively, accept orphaned Apex classes until bundles are cleaned up.\n\n> **Note**: Apex **test** classes (not referenced by GenAiFunction) can be deleted freely.\n\n---\n\n## Shared Dependencies Between Agent Versions\n\nWhen iterating on agent versions (V1 -> V2), Apex classes may be shared:\n\n- V1 agent (original) created `MyVerificationClass`, `MySetVariable`, etc.\n- V2 agent (rewrite) references the same Apex classes\n- Deleting V1's metadata does NOT free the Apex classes if V2 still references them\n- Orphaned V1 PluginDefinitions (PlannerId=null) also retain references\n\n**Best practice**: Track which Apex classes are shared across agent versions. Only delete Apex classes after confirming no active agent OR orphaned PluginDefinition references them.\n\n---\n\n## Recommended Cleanup Workflow\n\nWhen retiring old agent bundle versions:\n\n1. **Inventory**: List all GenAiPlannerBundle versions (`sf project retrieve start --metadata GenAiPlannerBundle`)\n2. **Identify active**: Query `BotVersion` for the currently active version number\n3. **Map dependencies**: For each old bundle, note which Apex/Flow classes it references\n4. **Orphan children**: PATCH each `GenAiPluginDefinition` to null `PlannerId`\n5. **Delete bundles**: DELETE each old `GenAiPlannerBundle` via Tooling API\n6. **Clean Apex** (optional): Delete unreferenced Apex classes after all referring bundles are gone\n\n> **Cross-reference**: See [known-issues.md](known-issues.md) Issue 21 (old bundles block Apex deletion) and Issue 22 (Flow version drift).\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":7091,"content_sha256":"c68610a6029c1bda08dcafc17d8bc8cc71cc297bc5fca238b8eaa3d42d2413b0"},{"filename":"references/migration-guide.md","content":"\u003c!-- Parent: sf-ai-agentscript/SKILL.md -->\n# Migration Guide: Agentforce Builder UI → Agent Script DSL\n\n> This guide maps every concept from the Agentforce Builder UI (Setup > Agents) to its Agent Script DSL equivalent. Use it when converting existing agents from the visual builder to code.\n\n---\n\n## Builder UI → Agent Script Mapping\n\n| # | Builder UI Concept | Agent Script Equivalent | Notes |\n|---|-------------------|------------------------|-------|\n| 1 | Topic name | `topic my_topic_name:` | Must use snake_case, max 80 chars |\n| 2 | Classification description | `description: \"...\"` on topic | Helps LLM decide when to enter topic |\n| 3 | Scope / \"What this topic can do\" | `reasoning.instructions` + available `actions` | Combined: instructions tell LLM what to do, actions tell it what tools are available |\n| 4 | Instructions (numbered text boxes) | `reasoning: instructions:` | Use `->` for logic/expressions, `\\|` for literal text |\n| 5 | Flow actions | `target: flow://FlowName` | Flow must be Autolaunched and Active |\n| 6 | Apex actions | `target: apex://ClassName` | Class must have `@InvocableMethod` |\n| 7 | Prompt Template actions | `target: prompt://TemplateName` | `generatePromptResponse://TemplateName` is also valid; template must exist in org |\n| 8 | \"Collect from user\" input | Slot-fill `...` operator | `with query = ...` — LLM extracts from conversation |\n| 9 | \"Show/Hide in conversation\" toggle | `filter_from_agent: True` | On action output definitions |\n| 10 | \"Require Confirmation\" toggle | `require_user_confirmation: True` | On action definitions |\n| 11 | Guard Conditions | `available when` clause | `available when @variables.verified == True` |\n\n---\n\n## What's New in Agent Script (No Builder UI Equivalent)\n\nThese capabilities exist ONLY in Agent Script — they cannot be configured through the Builder UI:\n\n| # | Feature | Description | Example |\n|---|---------|-------------|---------|\n| 1 | `before_reasoning:` hook | Deterministic pre-processing before LLM sees instructions | Set variables, load data, enforce gates |\n| 2 | `after_reasoning:` hook | Deterministic post-processing after LLM finishes | Logging, cleanup, audit flags |\n| 3 | Linked variables | Read-only variables bound to session/context sources | `source: @MessagingSession.Id` |\n| 4 | Per-topic `system.instructions` override | Different system prompts for different topics | Customize LLM persona per topic |\n| 5 | Topic delegation (supervision) | Call a topic as a function — it returns to caller | `@topic.expert` in `reasoning.actions` |\n| 6 | Conditional transitions with `if/else` | Deterministic routing based on variable state | `if @variables.risk >= 80: transition to @topic.escalation` |\n| 7 | Template arithmetic | Expression evaluation in text templates | `{[email protected] + 1}` |\n| 8 | Post-action loop pattern | Topic re-resolves after action, enabling check-at-top pattern | Deterministic post-action routing |\n| 9 | Latch variables | Force topic re-entry after user provides input | Workaround for topic selector limitations |\n| 10 | Connection block | Multi-channel escalation routing (messaging, voice) | Service Agent channel-specific escalation |\n\n---\n\n## Known Migration Gaps Requiring Manual Review\n\n| Gap | Why it matters | What to do |\n|-----|----------------|------------|\n| Legacy renderer / component override behavior | Rich rendering configured in legacy builders may not map 1:1 into Agent Script metadata | Re-test migrated actions in preview and runtime; be prepared for text fallback where the legacy surface used custom renderers |\n| Prompt-template input names containing `Input:` | Raw Agent Script requires quoted parameter names with `:` | Quote every prompt-template input key explicitly, for example `\"Input:customerId\"` |\n| Exact quoted field names | Some visual editors can rewrite specially quoted field names during round-trip save/reformat | Keep the raw `.agent` file in source control as the source of truth and diff after UI edits |\n| Legacy schema annotations | Some builder-era annotations and rendering hints do not have a direct Agent Script property | Record the intent in docs/comments and validate the migrated action manually instead of assuming automatic parity |\n| Separate topic/action YAML files | Builder-era or generated assets may suggest extra per-topic files | Consolidate topic logic and action definitions in the `.agent` file unless a separate packaging system explicitly requires otherwise |\n\n**Practical migration rule:** if a migrated action compiles but behaves differently, verify three things separately: (1) raw `.agent` syntax, (2) builder round-trip formatting, and (3) runtime behavior in the actual target surface.\n\n---\n\n## Side-by-Side: Simple Topic\n\n### Builder UI Configuration\n\n```\nTopic Name: Order Lookup\nClassification: Handles customer order status inquiries\n\nInstructions:\n 1. Ask the customer for their order number\n 2. Look up the order using the Get Order Details action\n 3. Share the order status with the customer\n\nActions:\n - Get Order Details (Flow: Get_Order_Details)\n Input: order_id (Collect from user)\n Output: status, estimated_delivery\n```\n\n### Agent Script Equivalent\n\n```yaml\ntopic order_lookup:\n description: \"Handles customer order status inquiries\"\n\n actions:\n get_order_details:\n description: \"Retrieves order information by order ID\"\n inputs:\n order_id: string\n description: \"Customer's order number\"\n outputs:\n status: string\n description: \"Current order status\"\n estimated_delivery: string\n description: \"Estimated delivery date\"\n target: \"flow://Get_Order_Details\"\n\n reasoning:\n instructions: ->\n # Post-action check (if order was already looked up)\n if @variables.order_status != \"\":\n | Your order status is: {[email protected]_status}\n | Estimated delivery: {[email protected]_delivery}\n\n | I can help you check your order status.\n | Please provide your order number.\n actions:\n lookup: @actions.get_order_details\n with order_id = ...\n set @variables.order_status = @outputs.status\n set @variables.estimated_delivery = @outputs.estimated_delivery\n```\n\n**Key differences in the Agent Script version:**\n- Explicit action definition with typed inputs/outputs and target\n- Post-action check pattern (at TOP of instructions) enables deterministic routing after the action completes\n- Variable injection (`{[email protected]}`) for dynamic text\n- Slot-fill (`...`) replaces \"Collect from user\"\n\n---\n\n## Side-by-Side: Verification Gate\n\n### Builder UI Configuration\n\n```\nTopic Name: Identity Verification\nClassification: Verifies customer identity before accessing account\n\nInstructions:\n 1. Ask the customer for their email address\n 2. Use the Verify Identity action to check their email\n 3. If verified, route to the Account Management topic\n 4. After 3 failed attempts, escalate to a human agent\n\nActions:\n - Verify Identity (Apex: VerifyIdentityAction)\n Input: email (Collect from user)\n Output: verified (boolean)\n - Go to Account Management (Topic Transition)\n - Escalate (Escalate to Agent)\n```\n\n### Agent Script Equivalent\n\n```yaml\nvariables:\n customer_verified: mutable boolean = False\n failed_attempts: mutable number = 0\n\ntopic identity_verification:\n description: \"Verifies customer identity before accessing account\"\n\n actions:\n verify_identity:\n description: \"Checks customer identity by email\"\n inputs:\n email: string\n description: \"Customer email address\"\n outputs:\n verified: boolean\n description: \"Whether identity was verified\"\n target: \"apex://VerifyIdentityAction\"\n\n reasoning:\n instructions: ->\n # Gate: Too many failures → escalate\n if @variables.failed_attempts >= 3:\n | Too many failed attempts. Connecting you with a human agent.\n transition to @topic.escalation\n\n # Gate: Already verified → proceed\n if @variables.customer_verified == True:\n transition to @topic.account_management\n\n | Please provide your email address to verify your identity.\n actions:\n verify: @actions.verify_identity\n with email = ...\n set @variables.customer_verified = @outputs.verified\n if @outputs.verified == False:\n set @variables.failed_attempts = @variables.failed_attempts + 1\n go_account: @utils.transition to @topic.account_management\n description: \"Proceed to account management\"\n available when @variables.customer_verified == True\n escalate_now: @utils.escalate\n description: \"Transfer to human agent\"\n```\n\n**Key differences in the Agent Script version:**\n- Deterministic gates at top of instructions (cannot be bypassed by LLM)\n- Explicit failure counter with arithmetic\n- `available when` guard ensures the \"proceed\" action is invisible until verified\n- `@utils.escalate` replaces the UI \"Escalate to Agent\" button\n\n---\n\n## Migration Checklist\n\nWhen converting a Builder UI agent to Agent Script:\n\n- [ ] **Export current config**: Document all topics, instructions, and actions from the UI\n- [ ] **Map each topic**: Use the mapping table above to translate concepts\n- [ ] **Identify implicit logic**: Builder UI often has implicit routing — make it explicit with `if/else`\n- [ ] **Add deterministic gates**: Convert any \"instruction-based\" security to `available when` guards\n- [ ] **Define variables**: Builder UI hides state management — declare all needed variables explicitly\n- [ ] **Set action targets**: Ensure all Flows/Apex classes are deployed with correct API names\n- [ ] **Test incrementally**: Validate each topic independently before combining\n- [ ] **Verify `default_agent_user`**: Must be a valid Einstein Agent User in the target org\n\n---\n\n## Common Migration Pitfalls\n\n| Pitfall | Description | Fix |\n|---------|-------------|-----|\n| Missing deterministic logic | Builder UI relies on LLM to follow instructions; Agent Script should enforce with code | Add `if/else` gates and `available when` guards |\n| Implicit routing | Builder UI auto-routes between topics; Agent Script needs explicit transitions | Add `@utils.transition` actions for each topic change |\n| Action naming conflicts | Builder UI action names may conflict with reserved words | Rename to avoid `description`, `label`, `escalate` |\n| Missing variable declarations | Builder UI manages state implicitly | Declare all mutable/linked variables explicitly |\n| Wrong instruction syntax | Builder UI text → `\\|` (pipe) syntax; conditional logic → `->` (arrow) syntax | Use arrow when you need `if`, `run`, `set`, or `transition` |\n| Renderer assumptions survived migration only in prose | Legacy descriptions may still mention a specific renderer even when the metadata no longer guarantees it | Re-validate rendering behavior in the target surface; treat description text as guidance, not a rendering contract |\n| Prompt template bindings copied literally without quotes | `Input:...` names parse differently in text DSL than in UI forms | Quote prompt-template input keys explicitly in `.agent` files |\n| Exact field names changed after save/re-open | Builder round-trip formatting can alter edge-case quoted keys | Diff the raw `.agent` after editing in UI if your action depends on an exact quoted field name |\n\n---\n\n*Last updated: 2026-04-03*\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":11617,"content_sha256":"489877f942f2f3555c366239b2633f967a4b2d6df29c7c46da64cb7d1777a0cc"},{"filename":"references/minimal-examples.md","content":"\u003c!-- Parent: sf-ai-agentscript/SKILL.md -->\n\n# Minimal Working Examples\n\n## Hello-World Agent Script\n\nA complete, deployable agent with one topic, one action, and conditional logic:\n\n```yaml\nconfig:\n developer_name: \"simple_agent\"\n description: \"A minimal working agent example\"\n agent_type: \"AgentforceServiceAgent\"\n default_agent_user: \"[email protected]\"\n\nsystem:\n messages:\n welcome: \"Hello! How can I help you today?\"\n error: \"Sorry, something went wrong.\"\n instructions: \"You are a helpful customer service agent.\"\n\nvariables:\n customer_verified: mutable boolean = False\n\ntopic main:\n description: \"Main conversation handler\"\n reasoning:\n instructions: ->\n if @variables.customer_verified == True:\n | You are speaking with a verified customer.\n | Help them with their request.\n else:\n | Please verify the customer's identity first.\n actions:\n verify: @actions.verify_customer\n description: \"Verify customer identity\"\n set @variables.customer_verified = @outputs.verified\n\nstart_agent entry:\n description: \"Entry point for all conversations\"\n reasoning:\n instructions: |\n Greet the customer and route to the main topic.\n actions:\n go_main: @utils.transition to @topic.main\n description: \"Navigate to main conversation\"\n```\n\n### Key Points\n\n- **`config` block**: `developer_name` must match the folder name (case-sensitive)\n- **`default_agent_user`**: Must be a valid Einstein Agent User in the target org — query with `sf data query`\n- **`instructions: ->`**: Procedural mode enables `if`/`else` and `run` directives\n- **`instructions: |`**: Literal mode for static text passed to the LLM\n- **`set @variables.X = @outputs.Y`**: Captures action output into mutable state\n- **`@utils.transition`**: Permanent handoff (does not return to calling topic)\n\n### Personalized Welcome Message\n\nUse quoted strings for static system messages, but switch `welcome` / `error` to template/block form when you need interpolation:\n\n```yaml\nvariables:\n user_preferred_name: linked string\n source: @MessagingSession.UserPreferredName__c\n description: \"Example linked variable source for first-turn personalization\"\n\nsystem:\n messages:\n welcome: |\n Hi {[email protected]_preferred_name}! How can I help you today?\n error: \"Sorry, something went wrong.\"\n instructions: \"You are a helpful customer service agent.\"\n```\n\n- Use `|` for dynamic welcome/error messages that include `{!...}` references.\n- Do **not** write dynamic system messages like `welcome: \"Hi {[email protected]_preferred_name}!\"` — that quoted form can render literally.\n- Keep quoted strings for static welcome/error text.\n- Keep conditional logic in `reasoning.instructions: ->`; `system.messages` should stay as static text or direct variable templates.\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":2832,"content_sha256":"66efbfc0fafec9deacfda87c2bd3e83d06cddc50c885d1025d80b1c03b6621c9"},{"filename":"references/official-sources.md","content":"\u003c!-- Parent: sf-ai-agentscript/SKILL.md -->\n# Official Sources Registry\n\n> Canonical Salesforce documentation URLs for Agent Script. Use this registry to verify syntax, resolve errors, and stay current with platform changes.\n\n---\n\n## Primary References (Agent Script Documentation)\n\n| # | Page | URL | Use When |\n|---|------|-----|----------|\n| 1 | Agent Script Overview | https://developer.salesforce.com/docs/ai/agentforce/guide/agent-script.html | Starting point, general concepts |\n| 2 | Language Characteristics | https://developer.salesforce.com/docs/ai/agentforce/guide/ascript-lang.html | Syntax model, whitespace rules, symbols |\n| 3 | Blocks Reference | https://developer.salesforce.com/docs/ai/agentforce/guide/ascript-blocks.html | Block structure, required fields |\n| 4 | Flow of Control | https://developer.salesforce.com/docs/ai/agentforce/guide/ascript-flow.html | Execution order, transitions, prompt construction |\n| 5 | Actions Reference | https://developer.salesforce.com/docs/ai/agentforce/guide/ascript-ref-actions.html | Action definitions, targets, I/O |\n| 6 | Variables Reference | https://developer.salesforce.com/docs/ai/agentforce/guide/ascript-ref-variables.html | Mutable, linked, types, sources |\n| 7 | Tools Reference | https://developer.salesforce.com/docs/ai/agentforce/guide/ascript-ref-tools.html | `@utils.*` utilities, transitions, topic delegation |\n| 8 | Utils Reference | https://developer.salesforce.com/docs/ai/agentforce/guide/ascript-ref-utils.html | Utility action details |\n| 9 | Instructions Reference | https://developer.salesforce.com/docs/ai/agentforce/guide/ascript-ref-instructions.html | Pipe vs arrow, resolution order |\n| 10 | Expressions Reference | https://developer.salesforce.com/docs/ai/agentforce/guide/ascript-ref-expressions.html | Conditionals and expression syntax |\n| 11 | Before/After Reasoning | https://developer.salesforce.com/docs/ai/agentforce/guide/ascript-ref-before-after-reasoning.html | Lifecycle hooks syntax |\n| 12 | Operators Reference | https://developer.salesforce.com/docs/ai/agentforce/guide/ascript-ref-operators.html | Comparison, logical, arithmetic |\n| 13 | Topic Blocks | https://developer.salesforce.com/docs/ai/agentforce/guide/ascript-blocks.html#topic-blocks | Topic structure, descriptions, reasoning/actions |\n| 14 | Agent Script Reference | https://developer.salesforce.com/docs/ai/agentforce/guide/ascript-reference.html | Keyword glossary and cross-reference hub |\n| 15 | Patterns Overview | https://developer.salesforce.com/docs/ai/agentforce/guide/ascript-patterns.html | Official pattern catalog |\n| 16 | Examples | https://developer.salesforce.com/docs/ai/agentforce/guide/ascript-example.html | Complete working examples |\n| 17 | Manage Agent Script Agents | https://developer.salesforce.com/docs/ai/agentforce/guide/ascript-manage.html | Configure, deploy, and test guidance |\n| 18 | Agent DX (CLI) | https://developer.salesforce.com/docs/ai/agentforce/guide/agent-dx.html | CLI commands, bundle structure |\n\n> **URL Prefix Note**: Agent Script docs migrated from `/docs/einstein/genai/guide/ascript-*` (older beta path) to `/docs/ai/agentforce/guide/ascript-*` (current path). Also note that some older per-topic/per-lifecycle URLs have been folded into newer pages or renamed (for example, `ascript-ref-before-after-reasoning.html` and `ascript-blocks.html#topic-blocks`).\n\n---\n\n## Official Pattern Pages\n\n| # | Page | URL | Use When |\n|---|------|-----|----------|\n| P1 | System Overrides | https://developer.salesforce.com/docs/ai/agentforce/guide/ascript-patterns-system-overrides.html | Topic-level `system.instructions` overrides |\n| P2 | Variables Pattern | https://developer.salesforce.com/docs/ai/agentforce/guide/ascript-patterns-variables.html | State sharing, slot filling, variable usage |\n| P3 | Transitions Pattern | https://developer.salesforce.com/docs/ai/agentforce/guide/ascript-patterns-transitions.html | `@utils.transition to`, deterministic vs LLM-selected routing |\n\n---\n\n## Recipe Repository\n\n| # | Page | URL | Content |\n|---|------|-----|---------|\n| 1 | Recipes Overview | https://developer.salesforce.com/sample-apps/agent-script-recipes/getting-started/overview | Getting started guide |\n| 2 | GitHub Repository | https://github.com/trailheadapps/agent-script-recipes | Source code, AGENT_SCRIPT.md rules |\n| 3 | Hello World | https://developer.salesforce.com/sample-apps/agent-script-recipes/language-essentials/hello-world | Minimal agent example |\n| 4 | Action Definitions | https://developer.salesforce.com/sample-apps/agent-script-recipes/action-configuration/action-definitions | Action config patterns |\n| 5 | Multi-Topic Navigation | https://developer.salesforce.com/sample-apps/agent-script-recipes/architectural-patterns/multi-topic-navigation | Topic routing patterns |\n| 6 | Error Handling | https://developer.salesforce.com/sample-apps/agent-script-recipes/architectural-patterns/error-handling | Error handling patterns |\n| 7 | Bidirectional Navigation | https://developer.salesforce.com/sample-apps/agent-script-recipes/architectural-patterns/bidirectional-navigation | Two-way topic transitions |\n| 8 | Advanced Input Bindings | https://developer.salesforce.com/sample-apps/agent-script-recipes/action-configuration/advanced-input-bindings | Slot-fill, fixed, variable binding |\n\n---\n\n## Diagnostic Decision Tree\n\nWhen something fails, use this tree to determine which doc page to fetch:\n\n```\nError or Ambiguity\n │\n ├─ Compilation / SyntaxError\n │ ├─ Block-level error → Fetch #3 (Blocks Reference)\n │ ├─ Expression error → Fetch #10 (Expressions) + #12 (Operators)\n │ └─ Action error → Fetch #5 (Actions Reference)\n │\n ├─ Action not executing\n │ ├─ Action defined but LLM doesn't pick it → Fetch #5 (Actions) + #7 (Tools)\n │ └─ Action target not found → Fetch #5 (Actions) + #18 (Agent DX)\n │\n ├─ Variable not updating\n │ ├─ Linked var empty → Fetch #6 (Variables)\n │ └─ Mutable not changing → Fetch #6 (Variables) + #9 (Instructions)\n │\n ├─ Topic transition wrong\n │ ├─ Wrong topic selected → Fetch #13 (Topic Blocks) + #8 (Utils)\n │ └─ Transition vs delegation confusion → Fetch #7 (Tools) + #13 (Topic Blocks)\n │\n ├─ Lifecycle hook issue\n │ └─ before/after_reasoning error → Fetch #11 (Before/After)\n │\n └─ New / unfamiliar syntax\n └─ Start with #1 (Overview), then narrow to specific reference\n```\n\n---\n\n## Fallback Search Patterns\n\nWhen a specific URL 404s or doesn't have the answer:\n\n```bash\n# Primary search pattern\nsite:developer.salesforce.com agent script \u003ctopic>\n\n# Recipe search\nsite:developer.salesforce.com sample-apps agent-script-recipes \u003ctopic>\n\n# GitHub issues (known bugs, community solutions)\nsite:github.com trailheadapps agent-script-recipes \u003cerror message>\n\n# Salesforce Stack Exchange\nsite:salesforce.stackexchange.com agent script \u003ctopic>\n```\n\n---\n\n## URL Health Check\n\nWhen verifying URLs, use WebFetch to confirm each resolves. If a URL redirects or 404s:\n\n1. Try swapping the URL prefix (see note above)\n2. Use the fallback search pattern\n3. If consistently broken, update this file and note the date\n\n---\n\n*Last updated: 2026-03-12*\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":7445,"content_sha256":"c33e4c5e311753dae02b8f2a31a5bbaafeb89fb116db5e0d5df4865c88812209"},{"filename":"references/patterns-quick-ref.md","content":"\u003c!-- Parent: sf-ai-agentscript/SKILL.md -->\n# Agent Script Patterns Quick Reference\n\n> Decision trees and cheat sheets for common Agent Script patterns\n\n---\n\n## Tool Selection Preflight\n\n```\nWhat kind of problem is this?\n│\n├─► Single-turn generation only?\n│ └─► PROMPT TEMPLATE / prompt action\n│\n├─► Fully static deterministic sequence?\n│ └─► FLOW / APEX\n│\n├─► Dynamic user input + branching + must-pass gates?\n│ └─► AGENT SCRIPT\n│\n└─► Mostly flexible conversation with a few hard controls?\n └─► AGENT SCRIPT + deterministic envelope\n```\n\n---\n\n## Pattern Selection Decision Tree\n\n### Which Architecture Pattern?\n\n```\nWhat's your agent's purpose?\n│\n├─► Multi-purpose (sales, support, orders)?\n│ └─► HUB AND SPOKE\n│ Central router → Specialized topics\n│\n├─► Sequential workflow (onboarding, checkout)?\n│ └─► LINEAR FLOW\n│ A → B → C pipeline\n│\n├─► Tiered support with escalation?\n│ └─► ESCALATION CHAIN\n│ L1 → L2 → L3 → Human\n│\n├─► Sensitive operations (payments, PII)?\n│ └─► VERIFICATION GATE\n│ Security check → Protected topics\n│\n└─► Multiple protected topics behind shared auth gate?\n └─► STATE GATE (OPEN GATE)\n 3-variable bypass with deferred routing\n```\n\n---\n\n## Routing Quick Rules\n\n- Use `description:` for routing and `label:` for display.\n- If one utterance fits two sibling topics, redesign.\n- Keep escalation and handoff topics limited to meta-intents.\n- Assume every new turn can trigger re-matching.\n\n### Quick Overlap Check\n\nAsk:\n- Would the same utterance reasonably match both topics?\n- Are you relying on instructions, not the topic description, to separate them?\n- Does one topic use broad umbrella terms like `help`, `support`, `account`, or `billing` that absorb adjacent intents?\n\n---\n\n## Node Type Decision Tree\n\n```\nWhat should this topic do?\n│\n├─► Route based on intent?\n│ └─► 🔵 ROUTING (Topic Selector)\n│\n├─► Security/identity check?\n│ └─► 🔵 VERIFICATION\n│\n├─► Fetch external data?\n│ └─► 🟡 DATA-LOOKUP\n│\n├─► Apply business rules?\n│ └─► 🟢 PROCESSING\n│\n└─► Transfer to human?\n └─► 🔴 HANDOFF\n```\n\n---\n\n## Variable Type Decision Tree\n\n```\nWhat kind of data is this?\n│\n├─► State that changes during conversation?\n│ │ (counters, flags, accumulated data)\n│ └─► MUTABLE\n│ `failed_attempts: mutable number = 0`\n│\n└─► Data from external source?\n │ (session, context, CRM)\n └─► LINKED\n `customer_id: linked string`\n ` source: @session.customerId`\n```\n\n---\n\n## Action Target Protocol Decision Tree\n\n```\nWhere should this action go?\n│\n├─► Data queries, record updates?\n│ └─► flow://\n│\n├─► Custom calculations, validation?\n│ └─► apex://\n│\n├─► LLM-generated summaries?\n│ └─► generatePromptResponse://\n│\n├─► Knowledge search, RAG?\n│ └─► retriever://\n│\n├─► External REST APIs?\n│ └─► externalService://\n│\n└─► Built-in SF actions (email, tasks)?\n └─► standardInvocableAction://\n```\n\n---\n\n## Deterministic vs Subjective Decision Tree\n\n```\nShould this be code-enforced or LLM-flexible?\n│\n├─► Security/safety requirement?\n│ └─► DETERMINISTIC (code)\n│\n├─► Financial threshold?\n│ └─► DETERMINISTIC (code)\n│\n├─► Counter/state management?\n│ └─► DETERMINISTIC (code)\n│\n├─► Conversational/greeting?\n│ └─► SUBJECTIVE (LLM)\n│\n├─► Context understanding needed?\n│ └─► SUBJECTIVE (LLM)\n│\n└─► Natural language generation?\n └─► SUBJECTIVE (LLM)\n```\n\n---\n\n## Control Placement Cheat Sheet\n\n| Need | Best home |\n|---|---|\n| Tone, behavior, conversational guidance | Instructions |\n| Facts, policy text, mutable rules | Retrieval / knowledge |\n| Visibility, scope, access control | Filters, `available when`, guarded transitions |\n| Mandatory order, must-run logic | Agent Script transitions, `run`, or Flow / Apex |\n| Long-lived context across turns | Variables |\n\n---\n\n## SOMA Pattern Decision Tree\n\n```\nDoes the conversation return to original agent?\n│\n├─► Yes, specialist handles sub-task\n│ └─► DELEGATION\n│ `@topic.specialist` (in reasoning.actions)\n│\n└─► No, permanent transfer\n ├─► To another topic?\n │ └─► HANDOFF `@utils.transition to @topic.X`\n │\n ├─► To human?\n │ └─► `@utils.escalate`\n │\n └─► To another agent?\n └─► `@agent.X` (Connections)\n```\n\n---\n\n## Transition Type Cheat Sheet\n\n| Syntax | Type | Control |\n|--------|------|---------|\n| `@utils.transition to @topic.X` | LLM-chosen | LLM decides when to use |\n| `transition to @topic.X` | Deterministic | Always executes when reached |\n| `@utils.escalate` | Permanent handoff | Human takeover |\n\n---\n\n## Instruction Resolution Order\n\n```\ninstructions: ->\n # 1. POST-ACTION CHECKS (at TOP - triggers on loop)\n if @variables.action_completed == True:\n run @actions.follow_up_action\n transition to @topic.next_step\n\n # 2. PRE-LLM DATA LOADING\n run @actions.load_required_data\n set @variables.data = @outputs.result\n\n # 3. DYNAMIC INSTRUCTIONS FOR LLM\n | Here is the context: {[email protected]}\n\n if @variables.condition:\n | Do this thing.\n else:\n | Do that thing.\n```\n\n**Why this order?**\n1. Post-action at TOP → triggers immediately on loop\n2. Data loading next → LLM needs current data\n3. Instructions last → LLM sees resolved values\n\n---\n\n## Common Patterns Quick Reference\n\n### Security Gate (Early Exit)\n\n```yaml\ninstructions: ->\n if @variables.failed_attempts >= 3:\n | Account locked due to too many attempts.\n transition to @topic.lockout # LLM never reasons\n```\n\n### Guarded Actions\n\n```yaml\nactions:\n process_refund: @actions.process_refund\n description: \"Issue refund\"\n available when @variables.customer_verified == True\n```\n\n### Post-Action Follow-Up\n\n```yaml\ninstructions: ->\n if @variables.refund_status == \"Approved\":\n run @actions.create_crm_case\n with customer_id = @variables.customer_id\n transition to @topic.success\n```\n\n### Data-Dependent Instructions\n\n```yaml\ninstructions: ->\n run @actions.get_account_tier\n set @variables.tier = @outputs.tier\n\n if @variables.tier == \"Gold\":\n | VIP treatment - offer 20% discount.\n else:\n | Standard customer service.\n```\n\n### Open Gate (State Gate)\n\n3-variable state machine that bypasses the LLM topic selector when a gate holds focus and routes users through an auth gate before accessing protected topics.\n\n```yaml\n# In topic_selector's before_reasoning — zero-credit bypass:\nbefore_reasoning:\n if @variables.open_gate == \"protected_workflow\":\n transition to @topic.protected_workflow\n if @variables.open_gate == \"authentication_gate\":\n transition to @topic.authentication_gate\n\n# In protected topic's before_reasoning — auth redirect:\nbefore_reasoning:\n if @variables.authenticated == False:\n set @variables.next_topic = \"protected_workflow\"\n set @variables.open_gate = \"authentication_gate\"\n transition to @topic.authentication_gate\n set @variables.open_gate = \"protected_workflow\"\n\n# EXIT_PROTOCOL — release gate when user changes intent:\nbefore_reasoning:\n set @variables.open_gate = \"null\"\n set @variables.next_topic = \"\"\n transition to @topic.topic_selector\n```\n\n> **Template**: [assets/patterns/open-gate-routing.agent](../assets/patterns/open-gate-routing.agent)\n\n---\n\n## Anti-Patterns to Avoid\n\n### ❌ Data Load After LLM Text\n\n```yaml\n# WRONG - LLM sees empty values\ninstructions: ->\n | Customer name: {[email protected]} # empty!\n run @actions.load_customer\n set @variables.name = @outputs.name\n```\n\n### ❌ Post-Action Check at Bottom\n\n```yaml\n# WRONG - Never triggers\ninstructions: ->\n | Help with refund.\n transition to @topic.main # Exits first!\n\n if @variables.refund_done: # Never reached\n run @actions.log_refund\n```\n\n### ❌ Mixing Tabs and Spaces\n\n```yaml\n# WRONG - Compilation error\nconfig:\n developer_name: \"MyAgent\" # 3 spaces\n agent_label: \"Label\" # 8 spaces - FAILS!\n```\n\n### ❌ Lowercase Booleans\n\n```yaml\n# WRONG - Agent Script uses Python-style\nis_verified: mutable boolean = true # WRONG\nis_verified: mutable boolean = True # CORRECT\n```\n\n---\n\n## Syntax Quick Reference\n\n| Pattern | Purpose |\n|---------|---------|\n| `instructions: ->` | Arrow syntax, enables expressions |\n| `instructions: \\|` | Pipe syntax, simple multi-line |\n| `if @variables.x:` | Conditional (pre-LLM) |\n| `run @actions.x` | Execute during resolution |\n| `set @var = @outputs.y` | Capture action output |\n| Curly-bang: {[email protected]} | Template injection |\n| `available when` | Control action visibility |\n| `transition to @topic.x` | Deterministic topic change |\n| `@utils.transition to` | LLM-chosen topic change |\n| `@utils.escalate` | Human handoff |\n\n---\n\n## The 6 Deterministic Building Blocks\n\n| # | Block | Example |\n|---|-------|---------|\n| 1 | Conditionals | `if @variables.failed_attempts >= 3:` |\n| 2 | Topic Filters | `available when @variables.cart_items > 0` |\n| 3 | Variable Checks | `if @variables.churn_risk >= 80:` |\n| 4 | Inline Actions | `run @actions.load_customer` |\n| 5 | Utility Actions | `@utils.transition`, `@utils.escalate` |\n| 6 | Variable Injection | Curly-bang: {[email protected]_name} |\n\n---\n\n## Implementation Best Practices\n\n> Migrated from the former `sf-ai-agentforce-legacy/references/patterns-and-practices.md` on 2026-02-07.\n\n### Pattern Details\n\n#### Lifecycle Events Pattern\n\n**File**: `assets/patterns/lifecycle-events.agent`\n\nExecute code automatically before and after every reasoning step.\n\n> **⚠️ Deployment Note**: The `run` keyword works in `reasoning.actions:` post-action blocks and `instructions: ->` blocks. It does NOT work reliably in `before_reasoning:` / `after_reasoning:` lifecycle blocks.\n\n```agentscript\ntopic conversation:\n before_reasoning:\n set @variables.turn_count = @variables.turn_count + 1\n run @actions.refresh_context # ⚠️ GenAiPlannerBundle only\n with [email protected]\n set @variables.context = @outputs.fresh_context\n\n reasoning:\n instructions: ->\n | Turn {[email protected]_count}: {[email protected]}\n\n after_reasoning:\n run @actions.log_analytics # ⚠️ GenAiPlannerBundle only\n with [email protected]_count\n```\n\n| ✅ Good Use Case | ❌ Not Ideal For |\n|------------------|------------------|\n| Track conversation metrics | One-time setup (use conditional) |\n| Refresh context every turn | Heavy processing (adds latency) |\n| Log analytics after each response | Actions that might fail often |\n\n#### Action Callbacks Pattern\n\n**File**: `assets/patterns/action-callbacks.agent`\n\nChain deterministic follow-up actions using the `run` keyword.\n\n> **⚠️ Deployment Note**: The `run` keyword works in `reasoning.actions:` post-action blocks and `instructions: ->` blocks. It does NOT work reliably in `before_reasoning:` / `after_reasoning:` lifecycle blocks.\n\n```agentscript\nprocess_order: @actions.create_order\n with [email protected]_id\n set @variables.order_id = @outputs.order_id\n run @actions.send_confirmation # ⚠️ GenAiPlannerBundle only\n with [email protected]_id\n run @actions.log_activity # ⚠️ GenAiPlannerBundle only\n with event=\"ORDER_CREATED\"\n```\n\n| ✅ Good Use Case | ❌ Not Ideal For |\n|------------------|------------------|\n| Audit logging (must happen) | Optional follow-ups (let LLM decide) |\n| Send notification after action | Complex branching logic |\n| Chain dependent actions | More than 1 level of nesting |\n\n**Critical Rule**: Only 1 level of `run` nesting allowed!\n\n#### Combining Patterns\n\nPatterns can be combined for complex scenarios:\n\n```\n┌─────────────────────────────────────────────────────────────┐\n│ LIFECYCLE + CALLBACKS + ROUTING │\n├─────────────────────────────────────────────────────────────┤\n│ │\n│ topic order_hub: │\n│ before_reasoning: ◄── Lifecycle │\n│ set @variables.turn_count = ... + 1 │\n│ │\n│ reasoning: │\n│ actions: │\n│ process: @actions.create │\n│ run @actions.notify ◄── Callback │\n│ run @actions.log │\n│ │\n│ consult: @utils.transition ◄── Routing │\n│ to @topic.specialist │\n│ │\n│ after_reasoning: ◄── Lifecycle │\n│ run @actions.update_metrics │\n│ │\n└─────────────────────────────────────────────────────────────┘\n```\n\n---\n\n### N-ary Boolean Expressions\n\nAgentScript supports **3 or more conditions** chained with `and`/`or`:\n\n```agentscript\n# Three+ conditions with AND\nbefore_reasoning:\n if @variables.is_authenticated and @variables.has_permission and @variables.is_active:\n transition to @topic.authorized\n\n# Three+ conditions with OR\nbefore_reasoning:\n if @variables.is_admin or @variables.is_moderator or @variables.is_owner:\n transition to @topic.elevated_access\n\n# In available when clauses\nreasoning:\n actions:\n process_return: @actions.handle_return\n description: \"Process customer return request\"\n available when @variables.eligible == True and @variables.order_id != None and @variables.tier != \"basic\"\n\n premium_action: @actions.premium_feature\n description: \"Premium tier feature\"\n available when @variables.tier == \"premium\" or @variables.tier == \"enterprise\" or @variables.is_trial_premium == True\n```\n\n**Key Points:**\n- Chain as many conditions as needed with `and` or `or`\n- Use `()` grouping for complex expressions: `(a and b) or (c and d)`\n- Works in `if` statements and `available when` clauses\n\n---\n\n### Naming Conventions\n\n| Element | Convention | Example |\n|---------|------------|---------|\n| Agent name | PascalCase with underscores | `Customer_Service_Agent` |\n| Topic name | snake_case | `order_management` |\n| Variable name | snake_case | `user_email` |\n| Action name | snake_case | `get_account_details` |\n| Transition action name | `go_to_` prefix | `go_to_checkout`, `go_to_support` |\n\n> 💡 Official GA docs recommend naming transition actions with a `go_to_` prefix for clarity (e.g., `go_to_checkout: @utils.transition to @topic.checkout`).\n\n---\n\n### Variable Management Best Practices\n\n#### Initialize with Defaults\n\n```agentscript\nvariables:\n # ✅ RECOMMENDED - Has default value (clearer intent)\n user_name: mutable string = \"\"\n description: \"Customer's full name\"\n\n order_count: mutable number = 0\n description: \"Number of orders in cart\"\n```\n\n> **Note**: Variables without defaults ARE supported. However, providing defaults is recommended for clarity.\n\n#### Use Appropriate Types\n\n| Data | Type | Example |\n|------|------|---------|\n| Names, IDs, text | `string` | `\"John Doe\"` |\n| Counts, amounts | `number` | `42`, `99.99` |\n| Flags, toggles | `boolean` | `True`, `False` |\n\n---\n\n### Topic Design Best Practices\n\n#### Provide Clear Descriptions\n\n```agentscript\n# ✅ GOOD - Specific and actionable\ntopic password_reset:\n description: \"Helps users reset forgotten passwords and unlock accounts\"\n\n# ❌ BAD - Too vague\ntopic password_reset:\n description: \"Password stuff\"\n```\n\n#### Keep Topics Focused (Single Responsibility)\n\n```agentscript\n# ✅ GOOD - Single responsibility\ntopic billing_inquiries:\n description: \"Answers questions about invoices, payments, and account balances\"\n\ntopic order_tracking:\n description: \"Provides order status and shipping updates\"\n\n# ❌ BAD - Too broad\ntopic customer_stuff:\n description: \"Handles billing, orders, support, and everything else\"\n```\n\n---\n\n### Security & Guardrails\n\n#### System-Level Guardrails\n\n```agentscript\nsystem:\n instructions:\n | You are a helpful customer service agent.\n |\n | IMPORTANT GUARDRAILS:\n | - Never share customer data with unauthorized parties\n | - Never reveal internal system details\n | - If unsure, escalate to a human agent\n```\n\n#### Don't Expose Internals\n\n```agentscript\n# ✅ GOOD - User-friendly error\ninstructions: ->\n if @variables.api_error == True:\n | I'm having trouble completing that request right now.\n\n# ❌ BAD - Exposes internals\ninstructions: ->\n if @variables.api_error == True:\n | Error: SQL timeout on server db-prod-03\n```\n\n---\n\n### Instructions Quality\n\n```agentscript\n# ✅ GOOD - Specific instructions\ninstructions: ->\n | Help the customer track their order.\n | Ask for the order number if not provided.\n | Provide the current status, estimated delivery, and tracking link.\n\n# ❌ BAD - Vague instructions\ninstructions: ->\n | Help with orders.\n```\n\n---\n\n### Common Syntax Pitfalls\n\n#### 1. Slot Filling Inside Conditionals\n\n```agentscript\n# ❌ WRONG\nif @variables.name is None:\n set @variables.name = ... # Fails!\n\n# ✅ CORRECT - Slot filling at top level\nset @variables.name = ...\n```\n\n#### 2. Description on @utils.transition\n\n```agentscript\n# ✅ CORRECT - description IS valid on @utils.transition\ngo_orders: @utils.transition to @topic.orders\n description: \"Route to orders\"\n\n# ✅ ALSO CORRECT - without description\ngo_orders: @utils.transition to @topic.orders\n```\n\n> **Note**: `description:` IS valid on `@utils.transition` (confirmed by TDD Val_Action_Properties and production 15-topic agent). Other properties like `label:`, `require_user_confirmation:`, and `include_in_progress_indicator:` are NOT valid on `@utils.transition` but ARE valid on action definitions with `target:` (TDD v2.2.0).\n\n#### 3. Missing Description on @utils.escalate\n\n```agentscript\n# ❌ WRONG\ntransfer: @utils.escalate # Fails!\n\n# ✅ CORRECT - Description required\ntransfer: @utils.escalate\n description: \"Transfer to human agent\"\n```\n\n#### 4. Empty Lifecycle Blocks\n\n```agentscript\n# ❌ WRONG\nbefore_reasoning:\n # Just a comment # Fails!\n\n# ✅ CORRECT - Remove empty blocks or add content\n```\n\n#### 5. Dynamic Action Invocation\n\n```agentscript\n# ❌ WRONG\ninvoke: {[email protected]} # Fails!\n\n# ✅ CORRECT - Define multiple actions, LLM auto-selects\nsearch_products: @actions.product_search\nsearch_orders: @actions.order_search\n```\n\n---\n\n### Validation Scoring Summary\n\n| Pattern | Points | Key Requirement |\n|---------|--------|-----------------|\n| Config block | 10 | All 4 required fields |\n| Linked variables | 10 | EndUserId, RoutableId, ContactId |\n| Topic structure | 10 | label, description, reasoning |\n| Language block | 5 | default_locale present |\n| Lifecycle blocks | 5 | Proper before/after structure |\n| Action callbacks | 5 | No nested run |\n| Error handling | 5 | Validation patterns |\n| Template expressions | 5 | {[email protected]} syntax |\n\n---\n\n### Slot Filling Reliability Patterns\n\n#### Problem: LLM Fails to Extract Values Correctly\n\n| Symptom | What Happened | Example |\n|---------|---------------|---------|\n| Empty JSON `{}` sent to action | LLM couldn't find value in conversation | User said \"look up my account\" without ID |\n| Wrong field names | LLM abbreviated or guessed | `_id` instead of `account_id` |\n| Wrong value extracted | LLM picked similar value from context | Picked Contact ID instead of Account ID |\n| Retry/crash cycles | No recovery path after failure | Agent keeps trying same extraction |\n\n**Root Cause:** The `...` syntax is **probabilistic** — the LLM infers what value to use. For critical inputs (IDs, amounts, required fields), this unreliability causes downstream failures.\n\n#### 5-Pattern Solution for Critical Inputs\n\n**Pattern 1: First-Interaction Collection** — Tell the LLM its PRIMARY GOAL:\n\n```agentscript\nreasoning:\n instructions: ->\n | YOUR PRIMARY GOAL: Collect the account ID from the user.\n | Do NOT proceed with any other actions until account_id is captured.\n |\n if @variables.account_id == \"\":\n | ⚠️ Account ID not yet collected. ASK the user for it.\n```\n\n**Pattern 2: Variable Setter Action** — Dedicated action to capture and validate:\n\n```agentscript\nactions:\n capture_account_id:\n description: \"Captures and validates the Salesforce Account ID from user\"\n inputs:\n account_id: string\n description: \"The 18-character Salesforce Account ID (starts with 001)\"\n is_required: True\n outputs:\n validated_id: string\n description: \"The validated account ID (empty if invalid)\"\n is_valid: boolean\n description: \"Whether the ID is valid\"\n target: \"flow://Validate_Account_Id\"\n```\n\n**Pattern 3: Single-Use Guard** — Make setter unavailable after capture:\n\n```agentscript\nreasoning:\n actions:\n validate_id: @actions.capture_account_id\n with account_id=...\n set @variables.account_id = @outputs.validated_id\n set @variables.account_id_validated = @outputs.is_valid\n available when @variables.account_id == \"\"\n```\n\n**Pattern 4: Null Guard Downstream Actions** — Block until validated:\n\n```agentscript\nreasoning:\n actions:\n do_research: @actions.research_account\n with [email protected]_id\n available when @variables.account_id_validated == True\n```\n\n**Pattern 5: Explicit Action References** — Guide the LLM:\n\n```agentscript\ninstructions: ->\n | To capture the account ID, use {[email protected]_account_id}.\n | This ensures the ID is validated before proceeding.\n```\n\n#### When NOT to Use Slot Filling\n\n| Use Slot Filling (`...`) | Use Variable/Fixed Value |\n|--------------------------|--------------------------|\n| Optional, non-critical inputs | Critical IDs (account, order, case) |\n| User preference inputs | Values that must be validated |\n| One-time collection | Values used across multiple actions |\n| Simple text descriptions | Values with specific formats (dates, IDs) |\n\n**Decision Rule:** If invalid input would cause downstream failure, use deterministic collection.\n\n> ⚠️ **Chained action restriction**: Slot filling (`...`) works for top-level action inputs but NOT for chained action inputs (via `run`). Chained actions run deterministically and cannot use LLM slot-filling.\n\n---\n\n### Pre-Deploy Checklist\n\nBefore deploying an agent, verify:\n\n- [ ] All topics have clear descriptions\n- [ ] All variables have descriptions and defaults\n- [ ] All actions have input/output descriptions\n- [ ] System guardrails are defined\n- [ ] Error handling is in place for critical operations\n- [ ] Navigation back to main menu from all topics\n- [ ] Template expressions use correct syntax `{[email protected]}`\n- [ ] Consistent indentation (tabs recommended)\n\n---\n\n\u003ca id=\"discovery-questions-pre-authoring\">\u003c/a>\n\n## Discovery Questions (Pre-Authoring)\n\n> Ask these questions before writing any Agent Script to clarify requirements and avoid rework. Sourced from [AGENT_SCRIPT.md](https://github.com/trailheadapps/agent-script-recipes).\n\n### Agent Identity\n- What is the agent's primary purpose?\n- Who are the end users (customers, employees, partners)?\n- What tone and persona should the agent adopt?\n- What languages must be supported?\n\n### Topics\n- What are the main conversation topics?\n- Which topics require security gates (authentication, authorization)?\n- What is the expected flow between topics (linear, hub-and-spoke, conditional)?\n- Are there topics that should delegate to sub-agents?\n\n### State Management\n- What data must persist across the conversation (mutable variables)?\n- What external data is needed at start (linked variables from session/context)?\n- Are there counters, flags, or accumulators that track progress?\n- What default values make sense for each variable?\n\n### Actions\n- Which actions require Flow targets vs. Apex targets?\n- What are the required inputs and outputs for each action?\n- Which actions need `available when` guards?\n- Are there actions that must chain deterministically (post-action follow-ups)?\n\n### Reasoning\n- Which decisions must be deterministic (code-enforced) vs. LLM-flexible?\n- Where should data be loaded (pre-LLM) vs. displayed (LLM instructions)?\n- Are there escalation thresholds or retry limits?\n- What guardrails must the system instructions enforce?\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":25629,"content_sha256":"e3af287729daa6c92d08be062f369f0edd602bff2bd9d96e18a7d837b0153c4e"},{"filename":"references/preview-test-loop.md","content":"\u003c!-- Parent: sf-ai-agentscript/SKILL.md -->\n# Preview Smoke Test Loop (Phase 3.5)\n\n> Rapid feedback on `.agent` files before publish — no CWC patch, no activate, no cross-skill delegation.\n\n---\n\n## 1. Overview\n\n**Purpose**: After Phase 3 validation passes, run 3-5 smoke test utterances against the agent using the programmatic `sf agent preview start/send/end --authoring-bundle` flow to catch topic routing, action invocation, and grounding issues *before* the formal publish/activate/test cycle.\n\n**Why this matters**: The `--authoring-bundle` flag compiles the `.agent` file server-side **without publishing** — no CustomerWebClient patch, no activation step. This enables ~15s iteration cycles (vs ~90s for publish+activate), letting Claude Code fix issues in a tight inner loop.\n\n**API used**: `/einstein/ai-agent/v1.1/preview/` endpoint (separate from the Runtime API used in Phase A testing). Traces are saved locally by the `sf` CLI.\n\n**Trace location**: `~/.sf/sfdx/agents/{agent}/sessions/{sid}/traces/{planId}.json`\n\n---\n\n## 2. Prerequisites\n\n| Requirement | Why | How to Verify |\n|-------------|-----|---------------|\n| Agent published at least once | Authoring bundle must exist in org for `--authoring-bundle` to work | `sf agent validate authoring-bundle --api-name AgentName -o ORG --json` |\n| `sf` CLI v2.121.7+ | Required for `--authoring-bundle` and programmatic preview subcommands | `sf version --json` |\n| Explicit preview mode chosen | Programmatic authoring-bundle preview now requires a mode flag | Pass exactly one of `--simulate-actions` or `--use-live-actions` to `sf agent preview start --authoring-bundle ...` |\n| Valid `default_agent_user` in `.agent` file | Preview runs as this user | Query the exact username from the `.agent` config and confirm: `IsActive = true`, `UserType != AutomatedProcess`, and `Profile.Name = 'Einstein Agent User'` |\n| Target org authenticated | Preview needs valid session | `sf org display -o ORG_ALIAS --json` |\n\n> ⚠️ **First-time agents**: If the agent has NEVER been published, `sf agent preview start --authoring-bundle` returns a 500 error. Run Phase 5 (publish + activate) first, then come back to Phase 3.5 for subsequent iterations.\n\n---\n\n## 3. Step-by-Step Workflow\n\n### Step 1: Start preview session using authoring bundle\n\n```bash\nSESSION_ID=$(sf agent preview start \\\n --authoring-bundle AgentName \\\n --simulate-actions \\\n --target-org ORG_ALIAS --json 2>/dev/null \\\n | jq -r '.result.sessionId')\n\necho \"Session: $SESSION_ID\"\n```\n\n### Step 2: Send smoke test utterance\n\n```bash\nRESPONSE=$(sf agent preview send \\\n --session-id \"$SESSION_ID\" \\\n --authoring-bundle AgentName \\\n --utterance \"I need help with my order\" \\\n --target-org ORG_ALIAS --json 2>/dev/null)\n\nPLAN_ID=$(echo \"$RESPONSE\" | jq -r '.result.messages[-1].planId')\nAGENT_RESPONSE=$(echo \"$RESPONSE\" | jq -r '.result.messages[-1].message')\n\necho \"Plan ID: $PLAN_ID\"\necho \"Response: $AGENT_RESPONSE\"\n```\n\n### Step 3: Repeat for each utterance\n\nSend each derived utterance (see \"Utterance Derivation\" below), capturing each `PLAN_ID` for trace analysis.\n\n```bash\n# Store plan IDs for later trace analysis\nPLAN_IDS=()\n\nfor UTTERANCE in \"Where is my order?\" \"I want to return this\" \"Tell me a joke\" \"My order #123 is late\"; do\n RESP=$(sf agent preview send \\\n --session-id \"$SESSION_ID\" \\\n --authoring-bundle AgentName \\\n --utterance \"$UTTERANCE\" \\\n --target-org ORG_ALIAS --json 2>/dev/null)\n PID=$(echo \"$RESP\" | jq -r '.result.messages[-1].planId')\n PLAN_IDS+=(\"$PID\")\n echo \"[$PID] $UTTERANCE → $(echo \"$RESP\" | jq -r '.result.messages[-1].message' | head -c 80)\"\ndone\n```\n\n### Step 4: End session and get trace path\n\n```bash\nTRACES_PATH=$(sf agent preview end \\\n --session-id \"$SESSION_ID\" \\\n --authoring-bundle AgentName \\\n --target-org ORG_ALIAS --json 2>/dev/null \\\n | jq -r '.result.tracesPath')\n\necho \"Traces at: $TRACES_PATH\"\n```\n\n### Step 5: Read trace for each plan ID\n\n```bash\n# Read a specific trace\nTRACE=\"$TRACES_PATH/$PLAN_ID.json\"\n```\n\n---\n\n## 4. Trace Analysis — 6 Checks with `jq`\n\nEach check includes: what to look for, the `jq` command, how to interpret results, and what to fix.\n\n### Check 1: Topic Routing\n\n**What**: Did the utterance route to the expected topic?\n\n```bash\njq '[.steps[] | select(.stepType == \"TransitionStep\") | .data.to]' \"$TRACE\"\n```\n\n**Expected**: Array contains the target topic name (e.g., `[\"order_mgmt\"]`).\n\n**If wrong or missing**:\n- Empty array → agent stayed in Topic Selector (descriptions too vague)\n- Wrong topic name → keyword overlap between topics\n\n**Fix**: Expand `topic X: description:` with keywords from the test utterance. See Fix Strategies §5.\n\n---\n\n### Check 2: Action Invocation\n\n**What**: Did the expected action get invoked?\n\n```bash\njq '[.steps[] | select(.stepType == \"FunctionStep\") | .data.function]' \"$TRACE\"\n```\n\n**Expected**: Array contains the target action name (e.g., `[\"Get_Order_Status\"]`).\n\n**If missing (empty array)**:\n- `available when:` guards too restrictive for the test context\n- Action `description:` doesn't clearly match what the user asked for\n- Action not listed in `reasoning.actions:` for this topic\n\n**Fix**: Relax `available when:` guards, improve action description. See Fix Strategies §5.\n\n---\n\n### Check 3: Wrong Action Selected\n\n**What**: An action was invoked, but it's the wrong one.\n\n```bash\n# Same jq as Check 2 — compare output against expected action name\njq '[.steps[] | select(.stepType == \"FunctionStep\") | .data.function]' \"$TRACE\"\n```\n\n**If wrong action**: Two actions have overlapping descriptions, and the planner picked the wrong one.\n\n**Fix**: Differentiate action descriptions with exclusion language. Add `available when:` guards to the wrong action. See Fix Strategies §5.\n\n---\n\n### Check 4: Grounding Assessment\n\n**What**: Is the agent's response grounded in actual data (not hallucinated)?\n\n```bash\njq '[.steps[] | select(.stepType == \"ReasoningStep\") | .data.groundingAssessment]' \"$TRACE\"\n```\n\n**Expected**: `\"GROUNDED\"` for all reasoning steps.\n\n**If `\"UNGROUNDED\"`**: The agent fabricated data instead of using action outputs or variable values.\n\n**Fix**: Add explicit data references in `instructions: ->` block. Reference specific `{[email protected]}` or `{[email protected]}` values. See Fix Strategies §5.\n\n---\n\n### Check 5: Safety Score\n\n**What**: Does the agent response meet safety thresholds?\n\n```bash\njq '.steps[] | select(.stepType == \"PlannerResponseStep\") | .data.safetyScore' \"$TRACE\"\n```\n\n**Expected**: `.overall >= 0.9`\n\n**If low**:\n- Agent revealing internal system details\n- Agent responding to harmful prompts without guardrails\n- Missing safety instructions in `system:` block\n\n**Fix**: Add explicit safety guidelines to `system: instructions:`. See Fix Strategies §5.\n\n---\n\n### Check 6: Tool Visibility\n\n**What**: Are the expected actions visible to the planner?\n\n```bash\njq '[.steps[] | select(.stepType == \"EnabledToolsStep\") | .data.enabled_tools]' \"$TRACE\"\n```\n\n**Expected**: Array includes the action names defined in the topic's `reasoning.actions:`.\n\n**If missing**:\n- `available when:` conditions not met for the test context\n- Action defined in wrong topic\n- Action `target:` protocol invalid (flow not deployed, apex class not found)\n\n**Fix**: Check `available when:` conditions match the test state. Verify `target:` exists in org.\n\n---\n\n## 5. Fix Strategies Reference\n\n| Failure | Target Block | Edit Strategy | Example |\n|---------|-------------|---------------|---------|\n| **TOPIC_NOT_MATCHED** | `topic X: description:` | Add keywords from test utterance | `\"Handle orders\"` → `\"Handle order queries, order status, package tracking, shipping updates\"` |\n| **ACTION_NOT_INVOKED** | `reasoning.actions: X description:` | Make description more trigger-specific | `\"Get order\"` → `\"Look up order status when user asks about their order, package, or delivery\"` |\n| **ACTION_NOT_INVOKED** | `available when:` | Relax guard condition | Remove overly restrictive `@variables.X == True` if variable isn't set yet |\n| **WRONG_ACTION_SELECTED** | Both competing `description:` fields | Differentiate with exclusion language | Add `\"NOT for returns\"` to order action, `\"ONLY for returns\"` to refund action |\n| **UNGROUNDED_RESPONSE** | `reasoning: instructions: ->` | Add explicit data references | `\"Help the customer\"` → `\"Help the customer using {[email protected]_data} from Get_Order action\"` |\n| **LOW_SAFETY_SCORE** | `system: instructions:` | Add safety guidelines | Add `CRITICAL: Never reveal internal system details or customer PII` |\n| **TOOL_NOT_VISIBLE** | `available when:` | Ensure guard matches test state | Set test variables before action, or remove guards for initial smoke test |\n\n### Fix Application Pattern\n\nAfter identifying a failure:\n\n1. **Edit the `.agent` file** — Apply the specific edit from the table above\n2. **LSP auto-validates** — The edit triggers LSP validation automatically (Phase 3 re-runs)\n3. **Re-run preview** — Start a new preview session with `--authoring-bundle` and re-send the failing utterance\n4. **Re-check trace** — Verify the fix resolved the issue\n5. **Max 3 iterations** — If still failing after 3 fix cycles, proceed to Phase 5 (publish) with warnings\n\n---\n\n## 6. Full Loop Walkthrough Example\n\nA fictional `OrderSupport` agent with 2 topics: `order_mgmt` and `returns`.\n\n### Initial `.agent` file (relevant excerpt)\n\n```yaml\ntopic order_mgmt:\n description: \"Handle orders\"\n reasoning:\n actions:\n get_order: @actions.Get_Order_Status\n description: \"Get order\"\n available when @variables.order_id != \"\"\n\ntopic returns:\n description: \"Handle returns\"\n reasoning:\n actions:\n process_return: @actions.Process_Return\n description: \"Process return\"\n```\n\n### Iteration 1: Derive utterances and run preview\n\n**Derived utterances** (one per topic + one guardrail + one multi-turn):\n1. `\"Where is my order?\"` → should route to `order_mgmt`\n2. `\"I want to return this\"` → should route to `returns`\n3. `\"Tell me a joke\"` → should trigger guardrail (off-topic)\n4. `\"My order #123 is late\"` → should route to `order_mgmt` and invoke `Get_Order_Status`\n\n**Run preview:**\n\n```bash\nSESSION_ID=$(sf agent preview start --authoring-bundle OrderSupport --simulate-actions --target-org dev --json 2>/dev/null | jq -r '.result.sessionId')\n\nRESP1=$(sf agent preview send --session-id \"$SESSION_ID\" --authoring-bundle OrderSupport --utterance \"Where is my order?\" --target-org dev --json 2>/dev/null)\nPID1=$(echo \"$RESP1\" | jq -r '.result.messages[-1].planId')\n\n# ... send remaining utterances, capture plan IDs ...\n\nTRACES_PATH=$(sf agent preview end --session-id \"$SESSION_ID\" --authoring-bundle OrderSupport --target-org dev --json 2>/dev/null | jq -r '.result.tracesPath')\n```\n\n**Analyze trace for utterance 1:**\n\n```bash\njq '[.steps[] | select(.stepType == \"TransitionStep\") | .data.to]' \"$TRACES_PATH/$PID1.json\"\n# Output: [] ← EMPTY! Topic not matched!\n```\n\n**Diagnosis**: \"Where is my order?\" didn't route to `order_mgmt` because the description `\"Handle orders\"` is too vague — the planner couldn't confidently match it.\n\n### Iteration 2: Fix topic description\n\n**Edit `.agent` file:**\n\n```yaml\n# BEFORE\ntopic order_mgmt:\n description: \"Handle orders\"\n\n# AFTER\ntopic order_mgmt:\n description: \"Handle order queries, order status, package tracking, shipping updates, delivery questions\"\n```\n\n**Re-run preview:**\n\n```bash\nSESSION_ID=$(sf agent preview start --authoring-bundle OrderSupport --simulate-actions --target-org dev --json 2>/dev/null | jq -r '.result.sessionId')\nRESP1=$(sf agent preview send --session-id \"$SESSION_ID\" --authoring-bundle OrderSupport --utterance \"Where is my order?\" --target-org dev --json 2>/dev/null)\nPID1=$(echo \"$RESP1\" | jq -r '.result.messages[-1].planId')\nTRACES_PATH=$(sf agent preview end --session-id \"$SESSION_ID\" --authoring-bundle OrderSupport --target-org dev --json 2>/dev/null | jq -r '.result.tracesPath')\n\njq '[.steps[] | select(.stepType == \"TransitionStep\") | .data.to]' \"$TRACES_PATH/$PID1.json\"\n# Output: [\"order_mgmt\"] ← PASS!\n\njq '[.steps[] | select(.stepType == \"FunctionStep\") | .data.function]' \"$TRACES_PATH/$PID1.json\"\n# Output: [] ← Get_Order_Status not invoked!\n```\n\n**Diagnosis**: Topic routing fixed, but `Get_Order_Status` action not invoked because:\n1. `available when @variables.order_id != \"\"` — but `order_id` is empty (user didn't provide it yet)\n2. Description `\"Get order\"` is too vague for the planner to match\n\n### Iteration 3: Fix action description and guard\n\n**Edit `.agent` file:**\n\n```yaml\n# BEFORE\nget_order: @actions.Get_Order_Status\n description: \"Get order\"\n available when @variables.order_id != \"\"\n\n# AFTER\nget_order: @actions.Get_Order_Status\n description: \"Look up order status when user asks about their order, package, delivery, or tracking\"\n with order_id=...\n```\n\n**Re-run preview** → all 4 utterances pass → **proceed to Phase 5 (publish)**.\n\n---\n\n## 7. Error Handling\n\n| Error | Cause | Resolution |\n|-------|-------|------------|\n| **500 from `preview start`** | Agent never published (authoring bundle doesn't exist in org) | Run Phase 5 first (publish + activate), then return to Phase 3.5 |\n| **`preview start` fails immediately for authoring bundle** | Missing execution-mode flag | Re-run with exactly one of `--simulate-actions` or `--use-live-actions` |\n| **Empty traces directory** | `sf agent preview end` may not write traces in all CLI versions | Check `--json` output of `preview end` for inline trace data; upgrade CLI if needed |\n| **`sessionId` is null** | Auth expired or org session invalid | Re-authenticate: `sf org login web --alias ORG_ALIAS` |\n| **`SessionAmbiguous (5)`** | Multiple cached sessions exist for the same agent | Run `sf agent preview sessions --json`, pick the right session ID, and retry with `--session-id` |\n| **No TransitionStep in trace** | Agent used Topic Selector but didn't route to any topic | Topic descriptions are too vague — add more keywords |\n| **No FunctionStep in trace** | Planner didn't select any action | Check `available when:` guards and action descriptions |\n| **`preview send` timeout** | Preview compilation taking too long | `.agent` file may be too complex; simplify or wait longer |\n| **Session already ended** | Sending to an expired/ended session | Start a new session with `preview start` |\n\n### Context Variable Limitations in Preview\n\n> ⛔ `sf agent preview` does NOT support context/session variable injection. There are no `--context`, `--session-var`, or `--variables` flags on any preview subcommand.\n\n| Variable Source | Works in Preview? | Alternative |\n|----------------|:-----------------:|-------------|\n| `@session.sessionID` | ❌ | Agent Runtime API with session context |\n| `@context.customerId` | ❌ | Agent Runtime API with `contextVariables` |\n| `@context.RoutableId` | ❌ | Agent Runtime API with `contextVariables` |\n| Mutable vars (defaults) | ✅ | Works normally via default values |\n| `with param=...` (slot-filling) | ✅ | Works normally via LLM extraction |\n\n> **Impact**: If your agent relies on `@context` or `@session` variables for routing or guards, those paths CANNOT be tested via `sf agent preview`. Use the Agent Runtime API (`/einstein/ai-agent/v1`) with `contextVariables` in the request body instead.\n\n---\n\n## 8. Phase 3.5 vs Phase 4 Comparison\n\n| Aspect | Phase 3.5 (Inner Loop) | Phase 4 (Formal Testing) |\n|--------|----------------------|------------------------|\n| **When** | Pre-publish (`--authoring-bundle`) | Post-publish + activate |\n| **Scope** | 3-5 smoke utterances | 20-100+ test cases |\n| **Depth** | Topic routing + action invocation | Multi-turn, re-matching, context preservation |\n| **Speed** | ~15s per iteration (no publish needed) | ~90s+ per iteration (publish + CWC + activate) |\n| **Fix loop** | Self-contained in sf-ai-agentscript | Cross-skill delegation to sf-ai-agentforce-testing |\n| **Skill** | sf-ai-agentscript (internal) | sf-ai-agentforce-testing (Phase A/B/C) |\n| **Auth** | sf CLI org auth (Named User JWT) | ECA + Client Credentials (for API testing) |\n| **Traces** | Local `~/.sf/sfdx/agents/` JSON files | Agent Runtime API response + STDM |\n| **Tools needed** | Bash, Read, Edit, `jq` | Python scripts, credential_manager, multi_turn_test_runner |\n\n---\n\n## Utterance Derivation Guide\n\nClaude Code derives smoke utterances from the `.agent` file itself:\n\n1. **One per non-start topic** — Based on `description:` keywords. Pick the most natural user phrasing.\n2. **One that should trigger each key action** — Match the action's `description:` to a realistic user request.\n3. **One off-topic utterance** — Tests guardrails (e.g., \"Tell me a joke\", \"What's the weather?\").\n4. **One multi-turn pair** — If agent has topic transitions, send two related utterances to test handoff (e.g., \"Check my order\" → \"Actually I want to return it\").\n\n**Example derivation** for an agent with topics `order_mgmt` (description: \"order status, tracking\") and `returns` (description: \"refunds, exchanges\"):\n\n```\n1. \"Where is my order?\" → order_mgmt (from description keywords)\n2. \"I want to return this\" → returns (from description keywords)\n3. \"Can you look up order 123\" → order_mgmt + Get_Order_Status action\n4. \"Tell me a joke\" → guardrail (off-topic)\n5. \"My order is late\" → \"Actually just process a return\" → topic transition\n```\n\n---\n\n## Quick Reference: The Full Loop\n\n```\nPhase 3 passes (LSP + CLI validation clean)\n │\n ▼\nDerive 3-5 utterances from .agent file\n │\n ▼\n┌─► sf agent preview start --authoring-bundle AgentName --simulate-actions\n│ │\n│ ▼\n│ sf agent preview send (for each utterance)\n│ │\n│ ▼\n│ sf agent preview end → get traces path\n│ │\n│ ▼\n│ Read traces with jq → run 6 checks\n│ │\n│ ├── All pass → Proceed to Phase 5 (publish) ✅\n│ │\n│ └── Failures found → Edit .agent file → LSP re-validates\n│ │\n│ └── iteration \u003c 3? ──────────────────────┐\n│ │ │\n│ ▼ │\n└───────────────── Yes ─┘ │\n │\n No (3 iterations exhausted) ◄──────────┘\n │\n ▼\n Phase 5 with warnings ⚠️\n```\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":18723,"content_sha256":"de32d794b382ed03ffa208ae943536f9da90d9fa3e9b37f7d343885d6c96f7f7"},{"filename":"references/production-gotchas.md","content":"\u003c!-- Parent: sf-ai-agentscript/SKILL.md -->\n\n# PRODUCTION GOTCHAS: Billing, Determinism & Performance\n\n## Credit Consumption Table\n\n> **Key insight**: Framework operations are FREE. Only actions that invoke external services consume credits.\n\n| Operation | Credits | Notes |\n|-----------|---------|-------|\n| `@utils.transition` | FREE | Framework navigation |\n| `@utils.setVariables` | FREE | Framework state management |\n| `@utils.escalate` | FREE | Framework escalation |\n| `if`/`else` control flow | FREE | Deterministic resolution |\n| `before_reasoning` | FREE | Deterministic pre-processing (see note below) |\n| `after_reasoning` | FREE | Deterministic post-processing (see note below) |\n| `reasoning` (LLM turn) | FREE | LLM reasoning itself is not billed |\n| Prompt Templates | 2-16 | Per invocation (varies by complexity) |\n| Flow actions | 20 | Per action execution |\n| Apex actions | 20 | Per action execution |\n| Any other action | 20 | Per action execution |\n\n> **✅ Lifecycle Hooks**: The `before_reasoning:` and `after_reasoning:` lifecycle hooks are validated. Content goes **directly** under the block (no `instructions:` wrapper). See \"Lifecycle Hooks\" section below for correct syntax.\n\n**Cost Optimization Pattern**: Fetch data once in `before_reasoning:`, cache in variables, reuse across topics.\n\n## Lifecycle Hooks: `before_reasoning:` and `after_reasoning:`\n\n```yaml\ntopic main:\n description: \"Topic with lifecycle hooks\"\n\n # BEFORE: Runs deterministically BEFORE LLM sees instructions\n before_reasoning:\n # Content goes DIRECTLY here (NO instructions: wrapper!)\n set @variables.pre_processed = True\n set @variables.customer_tier = \"gold\"\n\n # LLM reasoning phase\n reasoning:\n instructions: ->\n | Customer tier: {[email protected]_tier}\n | How can I help you today?\n\n # AFTER: Runs deterministically AFTER LLM finishes reasoning\n after_reasoning:\n # Content goes DIRECTLY here (NO instructions: wrapper!)\n set @variables.interaction_logged = True\n if @variables.needs_audit == True:\n set @variables.audit_flag = True\n```\n\n**Key Points:**\n- Content goes **directly** under `before_reasoning:` / `after_reasoning:` (NO `instructions:` wrapper)\n- Reliable primitives: `set`, `if`/`else`, `transition to`. `run` has inconsistent runtime behavior across bundle types — use it in `reasoning.actions:` or `instructions: ->` instead\n- `before_reasoning:` is FREE (no credit cost) - use for data prep\n- `after_reasoning:` is FREE (no credit cost) - use for logging, cleanup\n- `transition to` works in `after_reasoning:` — but if a topic transitions mid-reasoning, the original topic's `after_reasoning:` does NOT run\n\n**❌ WRONG Syntax (causes compile error):**\n```yaml\nbefore_reasoning:\n instructions: -> # ❌ NO! Don't wrap with instructions:\n set @variables.x = True\n```\n\n**✅ CORRECT Syntax:**\n```yaml\nbefore_reasoning:\n set @variables.x = True # ✅ Direct content under the block\n```\n\n## Supervision vs Handoff (Clarified Terminology)\n\n| Term | Syntax | Behavior | Use When |\n|------|--------|----------|----------|\n| **Handoff** | `@utils.transition to @topic.X` | Control transfers completely, child generates final response | Checkout, escalation, terminal states |\n| **Supervision** | `@topic.X` (as action reference) | Parent orchestrates, child returns, parent synthesizes | Expert consultation, sub-tasks |\n\n```yaml\n# HANDOFF - child topic takes over completely:\ncheckout: @utils.transition to @topic.order_checkout\n description: \"Proceed to checkout\"\n# → @topic.order_checkout generates the user-facing response\n\n# SUPERVISION - parent remains in control:\nget_advice: @topic.product_expert\n description: \"Consult product expert\"\n# → @topic.product_expert returns, parent topic synthesizes final response\n```\n\n**KNOWN BUG**: Adding ANY new action in Canvas view may inadvertently change Supervision references to Handoff transitions.\n\n## Action Output Flags for Zero-Hallucination Routing\n\n> **Key Pattern for Determinism**: Control what the LLM can see and say.\n\nWhen defining actions in Agentforce Assets, use these output flags carefully:\n\n| Flag | Effect | Use When |\n|------|--------|----------|\n| `filter_from_agent: True` | Hide from direct customer display (GA standard) | Output should stay hidden and does **not** need `is_used_by_planner` on the same field |\n| `is_displayable: False` | Hide from direct customer display (compile-valid alias) | Preferred hide-from-user flag when the planner still needs the same prompt output field |\n| `is_used_by_planner: True` | Planner can reason about the value | Decision-making, routing, response synthesis |\n\n> ⚠️ Do **not** combine `filter_from_agent` and `is_used_by_planner` on the same output field. That combination is a known blocking parser/runtime conflict.\n\n**Zero-Hallucination Intent Classification Pattern:**\n```yaml\n# In Agentforce Assets - Action Definition outputs:\noutputs:\n intent_classification: string\n is_displayable: False # Hide from direct display\n is_used_by_planner: True # Planner can use for routing decisions\n\n# In Agent Script - planner routes but the raw label is never shown directly:\ntopic intent_router:\n reasoning:\n instructions: ->\n run @actions.classify_intent\n set @variables.intent = @outputs.intent_classification\n\n if @variables.intent == \"refund\":\n transition to @topic.refunds\n if @variables.intent == \"order_status\":\n transition to @topic.orders\n```\n\n## `is_displayable: True` on Prompt Template Outputs\n\n> **Production impact**: Agent returns blank/empty responses when prompt template action outputs have `is_displayable: True` set.\n\n**The Problem**: Setting `is_displayable: True` (or toggling \"Show in conversation\" in the UI) on a prompt template action's output causes the platform to attempt direct rendering of the raw output. The rendering pipeline does not handle prompt template output format correctly, resulting in a blank response to the user — even though the trace shows the prompt template executed successfully.\n\n```yaml\n# ❌ WRONG — causes blank response for prompt template actions\noutputs:\n response_text: string\n is_displayable: True # Platform tries to render raw output → blank\n\n# ✅ CORRECT — let the reasoner synthesize the output\noutputs:\n response_text: string\n is_displayable: False # Hide raw output from direct display\n is_used_by_planner: True # LLM reads and synthesizes into response\n```\n\n**Key Points:**\n- This only affects prompt template actions (`prompt://` / `generatePromptResponse://` targets)\n- `is_displayable: True` works correctly on non-prompt actions (Flow, Apex) where the output is a simple string\n- The safest prompt-template default is `is_displayable: False` + `is_used_by_planner: True`\n- `filter_from_agent: True` is the GA-standard hide-from-user flag, but on prompt outputs it is a worse fit when the planner also needs the value because `filter_from_agent` and `is_used_by_planner` cannot be combined on the same field in this runtime\n- The reasoner naturally incorporates planner-visible outputs into its response — explicit display is unnecessary\n\n**If the prompt action ran but the response is still missing, check these next:**\n1. `is_displayable` is not `True`\n2. `is_used_by_planner` is `True` on the output that should influence the reply\n3. You did **not** combine `filter_from_agent` with `is_used_by_planner` on the same field\n4. The action output is not a structured wrapper that still needs field extraction before deterministic use\n\n> **Cross-reference**: See [known-issues.md](known-issues.md#issue-34) Issue 34.\n\n---\n\n## Raw `user_input` String Matching Is a Weak Deterministic Pattern\n\n> **Production impact**: Substring checks on the last utterance look simple, but they are brittle for deterministic routing, cancellation handling, and revision detection.\n\n**The Problem**: Branches such as `if @system_variables.user_input contains \"never mind\":` treat raw user text as if it were a normalized intent signal. That works inconsistently for real conversations because phrasing varies, punctuation varies, and the deterministic layer is not a robust intent classifier.\n\n```yaml\n# ❌ BRITTLE — raw utterance parsing as deterministic control flow\nif @system_variables.user_input contains \"never mind\":\n transition to @topic.cancel_request\n\n# ✅ SAFER — normalize the utterance first, then branch on explicit state\nrun @actions.classify_user_intent\n with utterance = @system_variables.user_input\n set @variables.cancel_requested = @outputs.cancel_requested\n\nif @variables.cancel_requested == True:\n transition to @topic.cancel_request\n```\n\n**Use raw string checks only for light heuristics.** If a branch must deterministically gate behavior, normalize it into:\n- a boolean (`cancel_requested`, `is_valid_url`)\n- an enum/string (`intent = \"cancel\"`)\n- or a pre-validated scalar returned by Flow/Apex\n\n---\n\n## Output Access Follows the Output Schema\n\n> **Production impact**: Deterministic comparisons fail when a target returns a wrapper object and the script treats `@outputs.X` like a plain scalar.\n\n**The Problem**: Some actions return structured outputs. In those cases, `@outputs.output` is an object envelope, not the final string/boolean you want to compare. The right accessor depends on the declared schema.\n\n```yaml\n# ❌ WRONG — assumes object output is already a boolean\nset @variables.is_after_hours = @outputs.output\n\n# ✅ CORRECT — access a concrete field only if the schema exposes it\nset @variables.is_after_hours = @outputs.output.value\n```\n\n**Guidance:**\n- If the action output is declared as `string`, `number`, or `boolean`, direct assignment is fine.\n- If the output is declared as `object` or another structured type, inspect the contract before you branch on it.\n- `.value` is a common wrapper field, but it is **not universal** — only use it when that field actually exists in the output schema.\n- For deterministic branches, the safest design is to flatten the target output in Flow/Apex so Agent Script receives an explicit scalar such as `is_after_hours: boolean`.\n\n---\n\n## Action I/O Metadata Properties\n\n> **Complete reference** for all metadata properties available on action definitions, inputs, and outputs.\n\n**Action-Level Properties:**\n\n| Property | Type | Effect |\n|----------|------|--------|\n| `label` | String | Display name in UI |\n| `description` | String | LLM reads this for decision-making |\n| `require_user_confirmation` | Boolean | Request user confirmation before execution (compiles; runtime no-op per Issue 6) |\n| `include_in_progress_indicator` | Boolean | Show spinner during execution |\n| `progress_indicator_message` | String | Custom spinner text |\n\n**Input Properties:**\n\n| Property | Type | Effect |\n|----------|------|--------|\n| `description` | String | Explains parameter to LLM |\n| `label` | String | Display name in UI |\n| `is_required` | Boolean | Marks input as mandatory for LLM |\n| `is_user_input` | Boolean | LLM extracts value from conversation |\n| `complex_data_type_name` | String | Lightning type mapping |\n\n**Output Properties:**\n\n| Property | Type | Effect |\n|----------|------|--------|\n| `description` | String | Explains output to LLM |\n| `label` | String | Display name in UI |\n| `filter_from_agent` | Boolean | `True` = hide from user display (GA standard) |\n| `is_displayable` | Boolean | `False` = hide from user (compile-valid alias). Prefer this on prompt outputs when the planner still needs the value |\n| `is_used_by_planner` | Boolean | `True` = LLM can reason about value |\n| `developer_name` | String | Overrides the parameter's developer name |\n| `complex_data_type_name` | String | Lightning type mapping |\n\n> **Cross-reference**: `filter_from_agent: True` is the GA standard name. `is_displayable: False` is a compile-valid alias.\n\n> ⚠️ For prompt-template outputs specifically, prefer `is_displayable: False` when the planner still needs the output. `filter_from_agent` is not a safe drop-in replacement there if you also need `is_used_by_planner`, because those two flags conflict on the same field.\n\n**User Input Pattern** (`is_user_input: True`):\n```yaml\ninputs:\n customer_name: string\n description: \"Customer's full name\"\n is_user_input: True # LLM pulls from what user already said\n is_required: True # Must have a value before action executes\n```\n\n## Action Chaining with `run` Keyword\n\n> **Known quirk**: Parent action may complain about inputs needed by chained action - this is expected.\n\n```yaml\nprocess_order: @actions.create_order\n with customer_id = @variables.customer_id\n run @actions.send_confirmation # Chains after create_order completes\n set @variables.order_id = @outputs.id\n```\n\n**KNOWN BUG**: Chained actions with Prompt Templates don't properly map inputs using `Input:Query` format.\n\n> **📖 For prompt template action definitions, input binding syntax, and grounded data patterns**, see [references/action-prompt-templates.md](../references/action-prompt-templates.md).\n\n## Latch Variable Pattern for Topic Re-entry\n\n> **Problem**: Topic selector doesn't properly re-evaluate after user provides missing input.\n\n**Solution**: Use a \"latch\" variable to force re-entry:\n\n```yaml\nvariables:\n verification_in_progress: mutable boolean = False\n\nstart_agent topic_selector:\n reasoning:\n instructions: ->\n if @variables.verification_in_progress == True:\n transition to @topic.verification\n | How can I help you today?\n actions:\n start_verify: @topic.verification\n description: \"Start identity verification\"\n set @variables.verification_in_progress = True\n\ntopic verification:\n reasoning:\n instructions: ->\n | Please provide your email to verify your identity.\n actions:\n verify: @actions.verify_identity\n with email = ...\n set @variables.verified = @outputs.success\n set @variables.verification_in_progress = False\n```\n\n## Loop Protection Guardrail\n\n> Agent Scripts have a built-in guardrail that limits iterations to approximately **3-4 loops** before breaking out and returning to the Topic Selector.\n\n**Best Practice**: Map out your execution paths and test for unintended circular references between topics.\n\n## Token & Size Limits\n\n| Limit Type | Value | Notes |\n|------------|-------|-------|\n| Max response size | 1,048,576 bytes (1MB) | Per agent response |\n| Plan trace limit (Frontend) | 1M characters | For debugging UI |\n| Transformed plan trace (Backend) | 32k tokens | Internal processing |\n| Active/Committed Agents per org | 100 max | Org limit |\n\n## Progress Indicators\n\n```yaml\nactions:\n fetch_data: @actions.get_customer_data\n description: \"Fetch customer information\"\n include_in_progress_indicator: True\n progress_indicator_message: \"Fetching your account details...\"\n```\n\n## VS Code Pull/Push NOT Supported\n\n```bash\n# ❌ ERROR when using source tracking:\nFailed to retrieve components using source tracking:\n[SfError [UnsupportedBundleTypeError]: Unsupported Bundle Type: AiAuthoringBundle\n\n# ✅ WORKAROUND - Use CLI directly:\nsf project retrieve start -m AiAuthoringBundle:MyAgent\nsf agent publish authoring-bundle --api-name MyAgent -o TARGET_ORG\n```\n\n## Reserved `@InvocableVariable` Keywords\n\n> **Validated March 2026**: Certain common words cannot be used as `@InvocableVariable` names in Apex classes called by Agent Script. Using them causes \"SyntaxError: Unexpected '{keyword}'\" during agent script compilation.\n\n**Reserved names (cannot use as `@InvocableVariable`):**\n\n| Reserved Name | Workaround | Example |\n|---------------|------------|---------|\n| `model` | `vehicle_model`, `data_model`, `model_name` | `@InvocableVariable public String vehicle_model;` |\n| `description` | `issue_description`, `desc_text`, `description_field` | `@InvocableVariable public String issue_description;` |\n| `label` | `label_text`, `display_label`, `label_field` | `@InvocableVariable public String label_text;` |\n\n**How it manifests:**\n- Apex compiles and deploys successfully (these are valid Apex identifiers)\n- Error only appears when the Agent Script compiler processes the action's I/O schema\n- Error message: `SyntaxError: Unexpected 'model'` (or `description`, `label`)\n- Fix: Rename the `@InvocableVariable` in Apex, redeploy, then republish the agent\n\n> **Cross-reference**: These same words are also reserved as Agent Script variable/field names. See [SKILL.md](../SKILL.md) reserved field names section.\n\n## Boolean-to-String Coercion Hazard\n\n> **Production impact**: Non-deterministic \"Something went wrong\" (SWW) errors scattered across turns. Extremely difficult to diagnose because failures are intermittent.\n\n**The Problem**: When a Flow or Apex action outputs a `Boolean` value and you capture it into a `mutable string` variable, then compare with `== \"true\"`, the coercion is non-deterministic. Some turns coerce `True` -> `\"true\"` correctly; others produce `\"True\"`, `\"1\"`, or empty string — causing silent routing failures.\n\n```yaml\n# ❌ HAZARDOUS — Boolean output into string variable\nvariables:\n verified: mutable string = \"false\"\n\nactions:\n verify: @actions.verify_user\n target: \"flow://Verify_User\"\n outputs:\n verified_result: boolean # Flow outputs Boolean\n\n# In reasoning:\nset @variables.verified = @outputs.verified_result # Boolean -> string coercion\nif @variables.verified == \"true\": # Non-deterministic match!\n transition to @topic.protected_area\n```\n\n**Fix Option A** (preferred): Use `mutable boolean` and compare with `== True`:\n```yaml\nvariables:\n verified: mutable boolean = False\n\n# In reasoning:\nset @variables.verified = @outputs.verified_result # Boolean -> boolean, no coercion\nif @variables.verified == True: # Deterministic!\n transition to @topic.protected_area\n```\n\n**Fix Option B**: Have the Flow/Apex output an explicit `String` (\"true\"/\"false\") instead of Boolean:\n```yaml\n# Flow/Apex outputs String \"true\" or \"false\" explicitly\noutputs:\n verified_result: string # Already a string, no coercion\n\n# In reasoning:\nset @variables.verified = @outputs.verified_result # String -> string\nif @variables.verified == \"true\": # Deterministic!\n```\n\n**Production validation**: Switching from Boolean->String coercion to explicit String output dropped SWW errors from scattered (multiple per session) to 1/61 turns (on an unrelated external callout).\n\n---\n\n## Escalation Fallback Loop\n\n> **Production impact**: Agent enters infinite escalation loop when no human agents are available — user sees repeated \"Connecting you...\" messages until the 3-4 loop guardrail kicks in.\n\n**The Problem**: `@utils.escalate` does not return a success/failure status. When no human agents are available (outside business hours, no Omni-Channel agents online), the escalation silently fails and the conversation re-enters the escalation topic, triggering the same logic again.\n\n**Fix**: Use a latch variable in `before_reasoning:` to detect re-entry and route to a fallback:\n\n```yaml\nvariables:\n escalation_attempted: mutable boolean = False\n\ntopic escalation:\n description: \"Escalate to human agent\"\n\n before_reasoning:\n if @variables.escalation_attempted == True:\n transition to @topic.leave_message\n set @variables.escalation_attempted = True\n\n reasoning:\n instructions: ->\n | Let me connect you with a support specialist.\n actions:\n handoff: @utils.escalate\n description: \"Transfer to human agent\"\n```\n\n**Key Points:**\n- The `before_reasoning:` check is FREE (no credit cost) and runs deterministically before the LLM\n- Always provide a graceful fallback topic (leave message, callback, self-service links)\n- This pattern also prevents credit waste — each failed escalation loop costs LLM reasoning credits\n\n> **Cross-reference**: See [known-issues.md](known-issues.md#issue-31) Issue 31. See [fsm-architecture.md](fsm-architecture.md) § Pattern 5 — Escalation with Availability Check.\n\n---\n\n## Language Block Quirks\n\n- Hebrew and Indonesian appear **twice** in the language dropdown\n- Selecting from the second set causes save errors\n- Use `adaptive_response_allowed: True` for automatic language adaptation\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":20471,"content_sha256":"f100179ae4eee28cbeb7a945d84ec32472d3f94cffc1ae2fcc667a32f4f87f1e"},{"filename":"references/scoring-rubric.md","content":"\u003c!-- Parent: sf-ai-agentscript/SKILL.md -->\n\n# Scoring System (100 Points)\n\n## Categories\n\n| Category | Points | Key Criteria |\n|----------|--------|--------------|\n| **Structure & Syntax** | 20 | Required blocks present, indentation consistency, valid identifiers, valid top-level wrappers |\n| **Deterministic Logic** | 25 | Security via `available when`, post-action checks, proper conditionals |\n| **Instruction Resolution** | 20 | Correct use of `->` vs `|`, template injection, action execution |\n| **FSM Architecture** | 15 | Clear topic separation, explicit transitions, state management |\n| **Action Configuration** | 10 | Correct protocols, input/output mapping, error handling |\n| **Deployment Readiness** | 10 | Valid `default_agent_user`, no compilation errors, metadata complete |\n\n## Scoring Rubric Details\n\n### Structure & Syntax (20 points)\n| Points | Criteria |\n|--------|----------|\n| 20 | All required blocks present, consistent indentation, valid identifiers |\n| 15 | Minor issues (e.g., inconsistent spacing within tolerance) |\n| 10 | Missing optional blocks that would improve clarity |\n| 5 | Significant structural issues (for example mixed indentation, invalid top-level wrappers, or repeated structural problems) |\n| 0 | Missing required blocks or compilation failures |\n\n> Top-level block order differences should be treated as style feedback unless they correspond to an actual validation or compiler issue.\n\n### Deterministic Logic (25 points)\n| Points | Criteria |\n|--------|----------|\n| 25 | All security actions guarded with `available when`, post-action patterns used |\n| 20 | Most guards present, minor gaps in deterministic enforcement |\n| 15 | Some security logic relies on prompts instead of guards |\n| 10 | Critical actions lack `available when` guards |\n| 0 | Security logic entirely prompt-based (LLM can bypass) |\n\n### Instruction Resolution (20 points)\n| Points | Criteria |\n|--------|----------|\n| 20 | Arrow syntax for complex logic, proper template injection, correct action execution |\n| 15 | Mostly correct, minor syntax issues |\n| 10 | Uses pipe syntax where arrow needed, template injection errors |\n| 5 | Incorrect phase ordering (data loads after LLM sees instructions) |\n| 0 | Fundamental misunderstanding of resolution order |\n\n### FSM Architecture (15 points)\n| Points | Criteria |\n|--------|----------|\n| 15 | Clear topic boundaries, explicit transitions, appropriate escalation paths |\n| 12 | Good structure with minor redundancy |\n| 9 | Topics too broad or transitions unclear |\n| 5 | Monolithic topic handling multiple concerns |\n| 0 | No topic separation, all logic in start_agent |\n\n### Action Configuration (10 points)\n| Points | Criteria |\n|--------|----------|\n| 10 | Correct protocols, proper I/O mapping, descriptions present |\n| 8 | Minor issues (missing descriptions) |\n| 5 | Wrong protocol for use case |\n| 2 | Input/output mapping errors |\n| 0 | Actions don't compile |\n\n### Deployment Readiness (10 points)\n| Points | Criteria |\n|--------|----------|\n| 10 | Valid user, clean validation, metadata complete |\n| 8 | Minor warnings |\n| 5 | Validation errors that need fixing |\n| 2 | Missing metadata files |\n| 0 | Cannot deploy |\n\n## Score Thresholds\n\n| Score | Rating | Action |\n|-------|--------|--------|\n| 90-100 | ⭐⭐⭐⭐⭐ Excellent | Deploy with confidence |\n| 80-89 | ⭐⭐⭐⭐ Very Good | Minor improvements recommended |\n| 70-79 | ⭐⭐⭐ Good | Review flagged issues before deploy |\n| 60-69 | ⭐⭐ Needs Work | Address issues before deploy |\n| \u003c60 | ⭐ Critical | **BLOCK** - Fix critical issues |\n\n## Score Report Format\n```\n📊 AGENT SCRIPT SCORE REPORT\n════════════════════════════════════════\n\nScore: 85/100 ⭐⭐⭐⭐ Very Good\n├─ Structure & Syntax: 18/20 (90%)\n├─ Deterministic Logic: 22/25 (88%)\n├─ Instruction Resolution: 16/20 (80%)\n├─ FSM Architecture: 12/15 (80%)\n├─ Action Configuration: 9/10 (90%)\n└─ Deployment Readiness: 8/10 (80%)\n\nIssues:\n⚠️ [Deterministic] Missing `available when` on process_refund action\n⚠️ [Resolution] Post-action check should be at TOP of instructions\n✓ All Structure & Syntax checks passed\n✓ All Action Configuration checks passed\n```\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":4302,"content_sha256":"22bd2f3d1c325fb2dd4cb732af3a0d781081173e062ab36677e224abf31742f0"},{"filename":"references/sources.md","content":"\u003c!-- Parent: sf-ai-agentscript/SKILL.md -->\n\n# Sources & Acknowledgments\n\n| Source | Contribution |\n|--------|--------------|\n| [trailheadapps/agent-script-recipes](https://github.com/trailheadapps/agent-script-recipes) | High-value secondary source for examples and emerging patterns. Mar 2026 audit against upstream commit `9c90176` confirmed: `developer_name` is now canonical, `description:` is the public config field used across the recipe corpus, `@actions.` prefix guidance is explicit, and `run @actions.X` vs reasoning-level utility resolution is documented. Caveats: repo still contains stale/inconsistent examples (`connections:` plural wrapper, mixed lifecycle-hook guidance, strong Employee-Agent bias, weak Service-Agent coverage). Treat as a corroborating source, not sole truth. See [upstream-recipes-audit.md](upstream-recipes-audit.md). |\n| [Official GA Documentation](https://developer.salesforce.com/docs/ai/agentforce/guide/agent-script.html) | GA alignment audit (v2.4.0): `filter_from_agent` as GA standard output property, `@system_variables.user_input`, `prompt://` shorthand, `developer_name` output property, `go_to_` naming convention, `is None` vs `== \"\"` distinction, topic-level `system:` overrides, variable `label` property, `timestamp`/`currency` type restrictions |\n| Salesforce Official Documentation | Core syntax, API references, deployment guides |\n| TDD Validation (this skill) | 13 validation agents confirming current-release syntax compatibility |\n| Tribal knowledge interviews | Canvas View bugs, VS Code limitations, credit consumption patterns |\n| [agentforce.guide](https://agentforce.guide/) | Unofficial but useful examples (note: some patterns don't compile in current release) |\n| @kunello ([PR #20](https://github.com/Jaganpro/sf-skills/pull/20)) | Prompt template `\"Input:fieldName\"` binding syntax, context-aware description overrides, `{[email protected]}` instruction reference patterns, callback behavior notes, error pattern catalog |\n| [aquivalabs/my-org-butler](https://github.com/aquivalabs/my-org-butler) | Official sources registry pattern, known-issues tracking structure, verification protocol, Builder UI → Agent Script migration guide, self-improvement protocol |\n\n> **⚠️ Note on Feature Validation**: Some patterns from external sources (e.g., `always_expect_input:`) do NOT compile in Winter '26. Action metadata properties (`label:`, `require_user_confirmation:`, etc.) are valid on **action definitions with targets** but NOT on `@utils.transition`. The `before_reasoning:`/`after_reasoning:` lifecycle hooks ARE valid but require **direct content** (no `instructions:` wrapper). This skill documents only patterns that pass TDD validation.\n\n> **Source-of-truth order for this skill**: official Salesforce docs → local validate/preview/publish evidence → upstream/community examples. When these disagree, prefer observed org behavior over recipe prose.\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":2929,"content_sha256":"034365a3671e4065db44be7868e28948dbfa2351a25cdc7c850892ec26667227"},{"filename":"references/syntax-reference.md","content":"\u003c!-- Parent: sf-ai-agentscript/SKILL.md -->\n# Agent Script Syntax Reference\n\n> Complete syntax guide for the Agent Script DSL. Your entire agent in one `.agent` file.\n\n---\n\n## Design Principles\n\n| Principle | Description |\n|-----------|-------------|\n| **Declarative Over Imperative** | Describe WHAT the agent should do, not HOW step-by-step |\n| **Human-Readable by Design** | Syntax resembles structured English - non-engineers can read it |\n| **Single File Portability** | Entire agent definition in one `.agent` file - copy/paste ready |\n| **Version Control Friendly** | Plain text works with Git - diff, review, rollback |\n\n---\n\n## Block Structure\n\n### Recommended Top-Level Block Convention\n\nUse the following order as this skill's preferred convention:\n\n```\nconfig → variables → system → connection → knowledge → language → start_agent → topic\n```\n\n| Block | Required | Purpose |\n|-------|----------|---------|\n| `config:` | ✅ Yes | Agent metadata and identification |\n| `variables:` | Optional | State management (mutable/linked) |\n| `system:` | ✅ Yes | Global messages and instructions |\n| `connection:` | Optional | Escalation routing (`connection messaging:` — singular, NOT `connections:`) |\n| `knowledge:` | Optional | Knowledge base configuration |\n| `language:` | Optional | Supported languages |\n| `start_agent:` | ✅ Yes | Entry point (exactly one) |\n| `topic:` | ✅ Yes | Conversation topics (one or more) |\n\n> ✅ **Evidence note**: Official Salesforce docs and examples present top-level blocks in varying sequences, and local validation evidence indicates both config-first and system-first orderings compile. Use one convention consistently, but do not treat top-level block order alone as a correctness rule.\n\n### Block Internal Ordering\n\nWithin `start_agent` and `topic` blocks, sub-blocks follow this order:\n\n```\ndescription → system → actions → reasoning → after_reasoning\n```\n\nWithin a `reasoning` block:\n\n```\ninstructions → actions\n```\n\n---\n\n## Block Definitions\n\n### 1. system: Block (Required)\n\n```yaml\nsystem:\n messages:\n welcome: \"Hello! How can I help?\"\n error: \"Sorry, something went wrong.\"\n instructions: \"You are a helpful assistant.\"\n```\n\n| Field | Purpose |\n|-------|---------|\n| `messages.welcome` | Initial greeting message |\n| `messages.error` | Fallback error message |\n| `instructions` | Global system prompt for the agent |\n\n> **Message syntax note**: Quoted strings are fine for **static** `welcome` / `error` messages. If a welcome or error message includes variable interpolation such as `{[email protected]_preferred_name}`, author that message in template/block form with `|` so the value is rendered in the system message. For first-turn personalization, prefer linked or session-backed variables.\n\n**❌ Wrong — quoted interpolation can render literally**\n```yaml\nsystem:\n messages:\n welcome: \"Hi {[email protected]_preferred_name}!\"\n error: \"Sorry, something went wrong.\"\n instructions: \"You are a helpful assistant.\"\n```\n\n**✅ Correct — use block form for dynamic system messages**\n```yaml\nsystem:\n messages:\n welcome: |\n Hi {[email protected]_preferred_name}! How can I help today?\n error: \"Sorry, something went wrong.\"\n instructions: \"You are a helpful assistant.\"\n```\n\n---\n\n### 2. config: Block (Required)\n\n```yaml\nconfig:\n developer_name: \"refund_agent\"\n description: \"Handles refund requests\"\n agent_type: \"AgentforceServiceAgent\"\n default_agent_user: \"[email protected]\"\n```\n\n| Field | Required | Purpose |\n|-------|----------|---------|\n| `developer_name` | ✅ Yes | Internal identifier (must match folder name, case-sensitive) |\n| `description` | ✅ Yes | Agent's goals and purpose |\n| `agent_type` | ✅ Yes | `AgentforceServiceAgent` or `AgentforceEmployeeAgent` |\n| `default_agent_user` | ⚠️ **REQUIRED** | Must be valid Einstein Agent User |\n| `agent_label` | Optional | Display name for the agent in UI (defaults to `developer_name` if omitted) |\n| `agent_description` | Optional / compatibility | Alternative config key accepted by local tooling; public docs/examples in this skill prefer `description` |\n| `company` | Optional | Company context for the agent |\n| `role` | Optional | Role/persona description for the agent |\n| `agent_version` | Optional / system-managed | Agent version metadata |\n| `enable_enhanced_event_logs` | Optional | Enables enhanced conversation logging (`True` / `False`) |\n| `user_locale` | Optional | User locale setting |\n\n> ⚠️ **Critical**: `default_agent_user` must exist in the org with the \"Einstein Agent User\" profile. Query: `SELECT Username FROM User WHERE Profile.Name = 'Einstein Agent User' AND IsActive = true`\n\n> ⚠️ **Official-docs caution**: `company`, `role`, `agent_version`, `enable_enhanced_event_logs`, and `user_locale` are documented by Salesforce. This skill now records them for completeness, but local field behavior has not been revalidated to the same depth as the core fields above.\n\n---\n\n### 3. variables: Block (Optional)\n\n```yaml\nvariables:\n # Mutable: State we track and modify\n failed_attempts: mutable number = 0\n customer_verified: mutable boolean = False\n label: \"Customer Verified\"\n order_ids: mutable list[string] = []\n order_notes: mutable string = \"\"\n description: |\n Notes collected during the order process.\n May contain multiple lines of customer input.\n\n # Linked: Read-only from external sources\n session_id: linked string\n source: @session.sessionID\n description: \"Current session identifier\"\n customer_id: linked string\n source: @context.customerId\n description: \"Customer ID from context\"\n```\n\n> 💡 Variables support a `label:` property for UI display names and multiline `description: |` using pipe syntax.\n\n> 💡 Variables also support a `visibility:` property with values `\"Internal\"` (default — hidden from end user, used by planner only) or `\"External\"` (visible to end user in the chat interface). Example: `visibility: \"External\"`\n\n#### Variable Types\n\n| Type | Description | Example |\n|------|-------------|---------|\n| `string` | Text values | `name: mutable string = \"\"` |\n| `number` | Numeric values | `count: mutable number = 0` |\n| `boolean` | True/false flags | `verified: mutable boolean = False` |\n| `object` | Structured data | `data: mutable object = {}` |\n| `date` | Calendar dates | `created: mutable date` |\n| `id` | Unique identifiers | `record_id: mutable id` |\n| `list[T]` | Arrays of type T | `items: mutable list[string] = []` |\n\n**Action-Only Types** (valid for action I/O, NOT for mutable/linked variables):\n\n| Type | Description |\n|------|-------------|\n| `datetime` | Date and time (action I/O only) |\n| `time` | Time of day (action I/O only) |\n| `integer` | Whole numbers (action I/O only) |\n| `long` | Large whole numbers (action I/O only) |\n| `timestamp` | Date and time (action I/O only) |\n| `currency` | Money values (action I/O only) |\n\n> ⚠️ **Undocumented variable types**: `timestamp` and `currency` compile as variable types but are absent from official GA documentation. Prefer `date` for date/time variables and `number` for currency values. These types are reliable for action I/O.\n\n> ⚠️ **`date` type fails in action I/O**: Using `date` as an action input or output type compiles but causes a runtime error (`'Date'`). Use `object` with `complex_data_type_name: \"lightning__dateType\"` instead. The `date` type works correctly for mutable/linked variables — the issue is only in action I/O. See [known-issues.md](known-issues.md#issue-28) Issue 28.\n\n#### Variable Modifiers\n\n| Modifier | Behavior | Use Case |\n|----------|----------|----------|\n| `mutable` | Read/write - can be changed during conversation | Counters, flags, accumulated state |\n| `linked` | Read-only - populated from external source | Session IDs, user profiles, context data |\n\n> ⚠️ **Booleans are capitalized**: Use `True`/`False`, not `true`/`false`\n\n---\n\n### 4. language: Block (Optional)\n\n```yaml\nlanguage:\n default: \"en_US\"\n supported: [\"en_US\", \"es_ES\", \"fr_FR\"]\n```\n\n---\n\n### 5. knowledge: Block (Optional)\n\n```yaml\nknowledge:\n knowledge_base: \"My_Knowledge_Base\"\n citations_enabled: False\n```\n\n| Field | Required | Purpose |\n|-------|----------|---------|\n| `knowledge_base` | ✅ Yes (if block present) | Name of the knowledge base to attach to the agent |\n| `citations_enabled` | Optional | `True`/`False` — controls whether knowledge citations are included in responses (default: `True` if omitted). Set to `False` if citations are not needed or if using NGA agents where citations do not render (Issue 32). |\n\n> 💡 Knowledge bases are configured in the Salesforce org and referenced by name. The knowledge block enables RAG (Retrieval Augmented Generation) capabilities for the agent.\n\n> ⚠️ **Required even without Data Library**: If your agent references knowledge in any topic but you don't have a Data Library configured, you still need a `knowledge:` block with `citations_enabled: False` to prevent compilation errors.\n\n---\n\n### 6. connection: Block (Optional)\n\n```yaml\n# Minimal form (no routing):\nconnection messaging:\n adaptive_response_allowed: True\n\n# Full form with escalation routing:\nconnection messaging:\n outbound_route_type: \"OmniChannelFlow\"\n outbound_route_name: \"flow://Route_from_Agent\"\n escalation_message: \"Connecting you with a specialist.\"\n adaptive_response_allowed: False\n```\n\n> ⚠️ Use `connection messaging:` (singular, NOT `connections:`). The plural `connections:` wrapper is invalid — see [known-issues.md](known-issues.md#issue-16) Issue 16.\n\n#### Supported Channels\n\n| Channel | Purpose |\n|---------|---------|\n| `messaging` | Chat/messaging channels (Enhanced Chat, Web Chat, In-App) |\n| `voice` | Voice/phone channels (Service Cloud Voice) |\n| `web` | Web-based channels |\n\n---\n\n### 7. topic: Block (Required - one or more)\n\n```yaml\ntopic main:\n description: \"Main conversation handler\"\n reasoning:\n instructions: |\n Help the user with their request.\n actions:\n do_something: @actions.my_action\n description: \"Action description\"\n```\n\n| Field | Purpose |\n|-------|---------|\n| `description` | Helps LLM understand topic purpose |\n| `reasoning.instructions` | Instructions for this topic |\n| `reasoning.actions` | Available actions in this topic |\n\n#### Topic-Level System Overrides\n\nTopics can override the agent-level `system:` instructions with their own `system:` block:\n\n```yaml\ntopic specialized_support:\n description: \"Handles technical support\"\n system:\n instructions: \"You are a technical support specialist. Be precise and methodical.\"\n reasoning:\n instructions: |\n Help with technical issues.\n```\n\n> The topic-level `system:` replaces (not appends to) the agent-level system instructions for that topic's reasoning context.\n\n---\n\n### 8. start_agent: Block (Required - exactly one)\n\n```yaml\nstart_agent entry:\n description: \"Entry point for conversations\"\n reasoning:\n instructions: |\n Greet the user and route appropriately.\n actions:\n go_main: @utils.transition to @topic.main\n description: \"Navigate to main topic\"\n```\n\n> 💡 The name can be anything - \"main\", \"entry\", \"topic_selector\" - just be consistent.\n\n---\n\n## Instruction Syntax\n\n### Pipe vs Arrow Syntax\n\n| Syntax | Use When | Example |\n|--------|----------|---------|\n| `instructions: \\|` | Simple multi-line text (no expressions) | `instructions: \\| Help the user.` |\n| `instructions: ->` | Complex logic with conditionals/actions | `instructions: -> if @variables.x:` |\n\n### Arrow Syntax (`->`) Patterns\n\n```yaml\nreasoning:\n instructions: ->\n # Conditional (resolves BEFORE LLM)\n if @variables.customer_verified == True:\n | Welcome back, verified customer!\n else:\n | Please verify your identity first.\n\n # Inline action execution\n run @actions.load_customer\n with customer_id = @variables.customer_id\n set @variables.customer_data = @outputs.data\n\n # Variable injection in text\n | Customer name: {[email protected]_name}\n\n # Deterministic transition\n if @variables.failed_attempts >= 3:\n transition to @topic.escalation\n```\n\n### Instruction Syntax Elements\n\n| Element | Syntax | Purpose |\n|---------|--------|---------|\n| Literal text | `\\| text` | Text that becomes part of LLM prompt |\n| Conditional | `if @variables.x:` | Resolves before LLM sees instructions |\n| Else clause | `else:` | Alternative path |\n| Inline action | `run @actions.x` | Execute action during resolution |\n| Set variable | `set @var = @outputs.y` | Capture action output |\n| Template injection | Curly-bang syntax: {[email protected]} | Insert variable value into text |\n| Deterministic transition | `transition to @topic.x` | Change topic without LLM |\n\n> ⚠️ **`else if` is NOT supported**: Agent Script does not have an `else if` keyword. Nested `if` inside `else:` is also invalid. Use compound conditions (`if A and B:`) or flatten to sequential `if` statements.\n\n### Multiline String Continuation\n\nLong literal text can span multiple lines using indented continuation:\n\n```yaml\ninstructions: |\n | This is a long instruction that\n continues on the next line without a pipe.\n```\n\nThe continued line is indented further than the `|` line and does NOT start with a new `|`.\n\n### Deterministic Resolution vs LLM-Driven Tool Use\n\n`instructions: ->` mixes two very different execution modes. Treat them separately when authoring.\n\n| Pattern | Who executes it | When it runs | Supports `...` | Best use |\n|---------|------------------|--------------|----------------|----------|\n| `run @actions.X` inside `instructions: ->` | Deterministic resolver | Before the LLM sees the final prompt | **No** | Preload data, normalize state, perform fixed control flow |\n| `reasoning.actions` tool invocation | LLM / planner | After instructions resolve | **Yes** | Slot filling, user-driven tool calls, flexible next-step selection |\n| `@utils.setVariables` from `reasoning.actions` | LLM / planner | After instructions resolve | **Yes** | Capture user-provided values into variables for later deterministic use |\n\n**Rule of thumb:** if a line begins with `run` inside `instructions: ->`, every `with` value must already exist as a literal, a variable, or a prior action output. Do not expect the resolver to slot-fill `...` in that phase.\n\n**❌ Wrong — deterministic `run` cannot slot-fill**\n```yaml\nreasoning:\n instructions: ->\n run @actions.send_verification_code\n with email = ...\n```\n\n**✅ Correct — let the LLM capture first, then use the saved variable**\n```yaml\nvariables:\n member_email: mutable string = \"\"\n\ntopic collect_email:\n reasoning:\n instructions: ->\n if @variables.member_email == \"\":\n | Ask the user for their email address, then call {[email protected]_email}.\n else:\n transition to @topic.send_code\n actions:\n save_email: @utils.setVariables\n description: \"Save the user's email address\"\n with member_email = ...\n\ntopic send_code:\n reasoning:\n instructions: ->\n run @actions.send_verification_code\n with email = @variables.member_email\n```\n\n---\n\n## Action Configuration\n\n### Action Declaration\n\n```yaml\nactions:\n action_name: @actions.my_action\n description: \"What this action does\"\n with input_param = @variables.some_value\n set @variables.result = @outputs.output_field\n available when @variables.is_authorized == True\n```\n\n### Action `source` Attribute\n\nActions imported from the Agentforce Asset Library require a `source:` attribute that matches the asset library's API name:\n\n```yaml\nactions:\n search_knowledge:\n description: \"Search the knowledge base\"\n source: \"My_Knowledge_Asset\" # Must match asset library API name exactly\n inputs:\n query: string\n outputs:\n answer: string\n target: \"retriever://My_Knowledge_Base\"\n```\n\n> ⚠️ If `source:` does not match the asset library API name exactly (case-sensitive), the action will fail at publish time with a reference resolution error.\n\n### Action Metadata Properties\n\nAction definitions with `target:` support the following metadata properties. These are NOT valid on `@utils.transition` utility actions.\n\n**Action-Level:**\n\n| Property | Type | Context | Notes |\n|----------|------|---------|-------|\n| `label` | String | Action def, topic, I/O | Display name in UI |\n| `description` | String | Action def, I/O | LLM decision-making context |\n| `require_user_confirmation` | Boolean | Action def | Compiles; runtime no-op (Issue 6) |\n| `include_in_progress_indicator` | Boolean | Action def | Shows spinner during execution |\n| `progress_indicator_message` | String | Action def | Custom spinner text |\n\n**Input-Level:**\n\n| Property | Type | Notes |\n|----------|------|-------|\n| `is_required` | Boolean | Marks input as mandatory — ⚠️ NOT enforced by planner (Issue 26) |\n| `is_user_input` | Boolean | LLM extracts from conversation |\n| `label` | String | Display name |\n| `description` | String | LLM context |\n| `complex_data_type_name` | String | Lightning type mapping |\n| `lightning:isPII` | Boolean | Marks field as PII for platform data handling and masking |\n\n**Output-Level:**\n\n| Property | Type | Notes |\n|----------|------|-------|\n| `filter_from_agent` | Boolean | `True` = hide from user display (GA standard name) |\n| `is_displayable` | Boolean | `False` = hide from user (compile-valid alias for `filter_from_agent`). ⚠️ Setting `True` on prompt template outputs causes blank response (Issue 34) |\n| `is_used_by_planner` | Boolean | `True` = LLM can reason about value |\n| `developer_name` | String | Overrides the parameter's developer name |\n| `label` | String | Display name |\n| `description` | String | LLM context |\n| `complex_data_type_name` | String | Lightning type mapping |\n| `lightning:isPII` | Boolean | Marks field as PII for platform data handling and masking |\n\n---\n\n### Two-Level Action System\n\nAgent Script uses a two-level system for actions. Understanding this distinction is critical:\n\n```\nLevel 1: ACTION DEFINITION (in topic's `actions:` block)\n → Has `target:`, `inputs:`, `outputs:`, `description:`\n → Specifies WHAT to call (e.g., \"flow://GetOrderStatus\")\n\nLevel 2: ACTION INVOCATION (in `reasoning.actions:` block)\n → References Level 1 via `@actions.name`\n → Specifies HOW to call it (`with`, `set` clauses)\n → Does NOT use `inputs:`/`outputs:` (use `with`/`set` instead)\n```\n\n**Complete Example:**\n```yaml\ntopic order_lookup:\n description: \"Look up order details\"\n\n # Level 1: DEFINE the action (with target + I/O schemas)\n actions:\n get_order:\n description: \"Retrieves order information by ID\"\n inputs:\n order_id: string\n description: \"Customer's order number\"\n outputs:\n status: string\n description: \"Current order status\"\n target: \"flow://Get_Order_Details\"\n\n reasoning:\n instructions: |\n Help the customer check their order status.\n # Level 2: INVOKE the action (with/set, NOT inputs/outputs)\n actions:\n lookup: @actions.get_order\n with order_id = ...\n set @variables.order_status = @outputs.status\n```\n\n> ⚠️ **I/O schemas are REQUIRED for publish**: Action definitions with only `description:` and `target:` (no `inputs:`/`outputs:`) will PASS LSP and CLI validation but FAIL server-side compilation with \"Internal Error.\" Always include complete I/O schemas in Level 1 definitions.\n\n---\n\n### Lifecycle Hooks: `before_reasoning:` and `after_reasoning:`\n\nLifecycle hooks enable deterministic pre/post-processing around LLM reasoning. They are FREE (no credit cost).\n\n```yaml\ntopic main:\n description: \"Topic with lifecycle hooks\"\n\n # BEFORE: Runs deterministically BEFORE LLM sees instructions\n before_reasoning:\n # Content goes DIRECTLY here (NO instructions: wrapper!)\n set @variables.turn_count = @variables.turn_count + 1\n if @variables.needs_redirect == True:\n transition to @topic.redirect\n\n # LLM reasoning phase\n reasoning:\n instructions: ->\n | Turn {[email protected]_count}: How can I help?\n\n # AFTER: Runs deterministically AFTER LLM finishes reasoning\n after_reasoning:\n # Content goes DIRECTLY here (NO instructions: wrapper!)\n set @variables.interaction_logged = True\n```\n\n**Key Rules:**\n- Content goes **directly** under the block (NO `instructions:` wrapper)\n- Reliable primitives: `set`, `if`/`else`, `transition to`\n- `run` behavior: lifecycle-hook syntax is valid, but `run @actions.X` inside `before_reasoning:` / `after_reasoning:` is not portable enough to treat as universally reliable. It has worked in some target-backed cases, but behavior is inconsistent across bundle types and org states. Safest guidance: keep lifecycle hooks focused on `set`, `if`/`else`, and `transition to`, and use `run` primarily in `reasoning.actions:` post-action chains or `instructions: ->` blocks.\n- `transition to` works in `after_reasoning:` blocks\n- If a topic transitions mid-execution, the original topic's `after_reasoning:` does NOT run\n- Both hooks are FREE (no credit cost) — use for data prep, logging, cleanup\n\n> 💡 Official GA docs show `after_reasoning:->` (arrow syntax). Our TDD validated the direct-content form. Both forms may work — we recommend the tested direct-content form.\n\n---\n\n### Action Target Protocols\n\n**Core Targets (Validated)**\n\n| Protocol | Use When | Status |\n|----------|----------|--------|\n| `flow://` | Data operations, business logic | ✅ Validated |\n| `apex://` | Custom calculations, validation | ✅ Validated |\n| `prompt://` / `generatePromptResponse://` | Grounded LLM responses | ✅ Validated |\n| `api://` | REST API callouts | ✅ Validated |\n| `retriever://` | RAG knowledge search | ✅ Validated |\n| `externalService://` | Third-party APIs via Named Credential | ✅ Validated |\n| `standardInvocableAction://` | Built-in SF actions | ✅ Validated |\n\n**Additional Targets (From agent-script-recipes)**\n\n| Protocol | Use When | Status |\n|----------|----------|--------|\n| `datacloudDataGraphAction://` | Data Cloud graph queries | ⚠️ Untested |\n| `datacloudSegmentAction://` | Data Cloud segment operations | ⚠️ Untested |\n| `triggerByKnowledgeSource://` | Knowledge-triggered actions | ⚠️ Untested |\n| `contextGrounding://` | Context grounding operations | ⚠️ Untested |\n| `predictiveAI://` | Einstein predictions | ⚠️ Untested |\n| `runAction://` | Sub-action execution | ⚠️ Untested |\n| `external://` | External services | ⚠️ Untested |\n| `copilotAction://` | Copilot actions | ⚠️ Untested |\n| `@topic.X` | Topic delegation (supervision) | ✅ Validated |\n\n> **Note**: Untested targets are documented in the official AGENT_SCRIPT.md rules. They may require specific licenses, org configurations, or future API versions.\n\n> 💡 `prompt://` is the official shorthand for `generatePromptResponse://`. Both forms resolve to the same target. Example: `target: \"prompt://Email_Draft_Template\"`\n\n### Utility Actions\n\n| Action | Purpose | Example |\n|--------|---------|---------|\n| `@utils.transition to @topic.x` | LLM-chosen topic navigation | `go_main: @utils.transition to @topic.main` |\n| `@utils.escalate` | Hand off to human agent | `escalate: @utils.escalate` |\n| `@utils.setVariables` | Set multiple variables | `set_vars: @utils.setVariables` |\n\n---\n\n## Resource References\n\n| Syntax | Purpose | Example |\n|--------|---------|---------|\n| `@variables.x` | Reference a variable | `@variables.customer_id` |\n| `@actions.x` | Reference an action | `@actions.process_refund` |\n| `@topic.x` | Reference a topic | `@topic.escalation` |\n| `@outputs.x` | Reference action output | `@outputs.status` |\n| `@session.x` | Reference session data | `@session.sessionID` |\n| `@context.x` | Reference context data (**Employee Agents on LEX only** — NOT available for Service Agents; see Issue 39) | `@context.userProfile` |\n| `@inputs.x` | Reference procedure input | `@inputs.account_number` ⚠️ Procedure context only — see Common Pitfalls |\n| `@system_variables.user_input` | Most recent user utterance | `@system_variables.user_input` |\n| `@knowledge.citations_url` | Knowledge citation URL config | `@knowledge.citations_url` |\n| `@knowledge.rag_feature_config_id` | RAG feature configuration ID | `@knowledge.rag_feature_config_id` |\n| `@knowledge.citations_enabled` | Whether citations are active | `@knowledge.citations_enabled` |\n\n> 💡 `@system_variables` is a separate namespace from `@variables`. The `user_input` system variable contains the customer's most recent utterance.\n\n> ⚠️ Treat `@system_variables.user_input` as raw text, not a durable deterministic intent signal. Direct guards like `if @system_variables.user_input contains \"never mind\":` are brittle for control-flow-critical routing. Prefer a Flow, Apex, or classifier action that normalizes the utterance into a boolean or enum variable first.\n\n#### Common Linked Variable Sources\n\nLinked variables commonly reference these external source patterns:\n\n| Source Pattern | Description | Example |\n|----------------|-------------|---------|\n| `@session.sessionID` | Current session identifier | Standard session context |\n| `@context.customerId` | Customer ID from context | **Employee Agents on LEX pages only** — NOT available for Service Agents (Issue 39) |\n| `@MessagingSession.{field}` | Messaging session fields (Service Agents) | `@MessagingSession.MessagingEndUserId` |\n| `@MessagingEndUser.{field}` | Messaging end user fields (Service Agents) | `@MessagingEndUser.Name`, `@MessagingEndUser.ContactId` |\n\n> ⚠️ **Agent Type Determines Source Availability**: `@context.*` sources resolve LEX page-level context — they only work for **Employee Agents** embedded on Lightning record pages. **Service Agents** (NGA / ExternalCopilot) must use `@MessagingSession.*` or `@MessagingEndUser.*` for channel context. Using `@context.*` in a Service Agent compiles but produces \"Unsupported data type\" at runtime. See [known-issues.md](known-issues.md#issue-39) Issue 39.\n\n```yaml\n# Common Messaging channel linked variables\nvariables:\n session_key: linked string\n source: @MessagingSession.MessagingSessionKey\n description: \"Unique messaging session key\"\n end_user_name: linked string\n source: @MessagingEndUser.Name\n description: \"End user display name from messaging channel\"\n contact_id: linked string\n source: @MessagingEndUser.ContactId\n description: \"Contact record ID linked to messaging end user\"\n```\n\n> 💡 `@MessagingSession` and `@MessagingEndUser` sources are available when the agent is deployed on a Messaging channel (Enhanced Chat, In-App, Web Chat). They are NOT available in Agent Builder Preview or `sf agent preview`.\n\n### Output Access Depends on the Declared Schema\n\nDo **not** assume `@outputs.X` is always a plain scalar. The correct access pattern depends on the action's declared output shape.\n\n```yaml\n# Scalar output → direct assignment is fine\nset @variables.order_status = @outputs.status\n\n# Structured output → keep it as an object, or access a concrete field\nset @variables.raw_result = @outputs.output\nset @variables.result_text = @outputs.output.value # if the output schema exposes 'value'\n```\n\n**Guidance:**\n- If the output is declared as `string`, `number`, or `boolean`, direct assignment is fine.\n- If the output is declared as `object` or another structured type, inspect the schema before branching on it.\n- Some actions wrap their primary scalar under a field such as `.value`; others expose named properties directly.\n- When a deterministic branch depends on the result, the safest design is to flatten the target output in Flow/Apex so Agent Script receives an explicit scalar like `is_after_hours: boolean` instead of a wrapper object.\n\n---\n\n## Whitespace Rules\n\n### Indentation\n\n| ✅ CORRECT | ❌ INCORRECT |\n|------------|-------------|\n| 2-space consistent | Mixed tabs and spaces |\n| 3-space consistent | Inconsistent spacing |\n| Tabs consistent | Tab in one block, spaces in another |\n\n> **CRITICAL**: Never mix tabs and spaces in the same file. This causes compilation errors.\n\n### Boolean Values\n\n| ✅ CORRECT | ❌ INCORRECT |\n|------------|-------------|\n| `True` | `true` |\n| `False` | `false` |\n\n---\n\n## Complete Example\n\n```yaml\nsystem:\n messages:\n welcome: \"Welcome to Pronto Support!\"\n error: \"Sorry, something went wrong. Let me connect you with a human.\"\n instructions: \"You are a helpful customer service agent for Pronto Delivery.\"\n\nconfig:\n developer_name: \"pronto_refund_agent\"\n description: \"Handles customer refund requests with churn risk assessment\"\n agent_type: \"AgentforceServiceAgent\"\n default_agent_user: \"[email protected]\"\n\nvariables:\n # Mutable state\n customer_verified: mutable boolean = False\n failed_attempts: mutable number = 0\n churn_risk_score: mutable number = 0\n refund_status: mutable string = \"\"\n\n # Linked from session\n customer_id: linked string\n source: @session.customerId\n description: \"Customer ID from messaging session\"\n\ntopic identity_verification:\n description: \"Verify customer identity before refund processing\"\n reasoning:\n instructions: ->\n if @variables.failed_attempts >= 3:\n | Too many failed attempts. Escalating to human agent.\n transition to @topic.escalation\n\n if @variables.customer_verified == True:\n | Identity verified. Proceeding to refund assessment.\n transition to @topic.refund_processor\n\n | Please verify your identity by providing your email address.\n actions:\n verify: @actions.verify_customer\n description: \"Verify customer by email\"\n set @variables.customer_verified = @outputs.verified\n\ntopic refund_processor:\n description: \"Process refund based on churn risk assessment\"\n reasoning:\n instructions: ->\n # Post-action check (triggers on loop after refund)\n if @variables.refund_status == \"Approved\":\n run @actions.create_crm_case\n with customer_id = @variables.customer_id\n transition to @topic.success\n\n # Pre-LLM: Load churn data\n run @actions.get_churn_score\n with customer_id = @variables.customer_id\n set @variables.churn_risk_score = @outputs.score\n\n # Dynamic instructions based on score\n | Customer churn risk: {[email protected]_risk_score}%\n\n if @variables.churn_risk_score >= 80:\n | HIGH RISK - Offer full cash refund to retain customer.\n else:\n | LOW RISK - Offer $10 store credit as goodwill.\n actions:\n process_refund: @actions.process_refund\n description: \"Issue the refund\"\n available when @variables.customer_verified == True\n set @variables.refund_status = @outputs.status\n\ntopic escalation:\n description: \"Escalate to human agent\"\n reasoning:\n instructions: |\n Apologize for the inconvenience and transfer to a human agent.\n actions:\n handoff: @utils.escalate\n description: \"Transfer to live support\"\n\ntopic success:\n description: \"Successful refund confirmation\"\n reasoning:\n instructions: |\n Thank the customer and confirm their refund has been processed.\n\nstart_agent topic_selector:\n description: \"Entry point - route to identity verification\"\n reasoning:\n instructions: |\n Greet the customer and begin identity verification.\n actions:\n start: @utils.transition to @topic.identity_verification\n description: \"Begin refund process\"\n```\n\n---\n\n## Expression Operators\n\n### Comparison Operators\n\n| Operator | Description | Example |\n|----------|-------------|---------|\n| `==` | Equal to | `if @variables.status == \"active\":` |\n| `!=` | Not equal to | `if @variables.status != \"closed\":` |\n| `\u003c` | Less than | `if @variables.count \u003c 10:` |\n| `\u003c=` | Less than or equal | `if @variables.count \u003c= 5:` |\n| `>` | Greater than | `if @variables.risk > 80:` |\n| `>=` | Greater than or equal | `if @variables.attempts >= 3:` |\n| `is` | Identity check | `if @variables.data is None:` |\n| `is not` | Negated identity check | `if @variables.data is not None:` |\n\n> **Note**: Use `!=` for not-equal comparisons. The `\u003c>` operator does NOT compile.\n\n### Logical Operators\n\n| Operator | Description | Example |\n|----------|-------------|---------|\n| `and` | Logical AND | `if @variables.verified == True and @variables.active == True:` |\n| `or` | Logical OR | `if @variables.status == \"open\" or @variables.status == \"pending\":` |\n| `not` | Logical NOT | `if not @variables.blocked:` |\n\n### Arithmetic Operators\n\n| Operator | Description | Example |\n|----------|-------------|---------|\n| `+` | Addition | `set @variables.count = @variables.count + 1` |\n| `-` | Subtraction | `set @variables.remaining = @variables.total - @variables.used` |\n\n> ⚠️ **NOT supported**: `*` (multiplication), `/` (division), `%` (modulo). For complex arithmetic, use a Flow or Apex action.\n\n### Access Operators\n\n| Operator | Description | Example |\n|----------|-------------|---------|\n| `.` | Property access | `@outputs.result.status` |\n| `[]` | Index access | `@variables.items[0]` |\n\n### Conditional Expression (Ternary-like)\n\n```yaml\n| Status: {[email protected] if @variables.status else \"pending\"}\n```\n\n### Expression Limitations (Sandboxed Python AST Subset)\n\nAgent Script expressions use a sandboxed subset of Python. Not all Python operations are available.\n\n**Supported:**\n\n| Category | Operations |\n|----------|-----------|\n| Arithmetic | `+`, `-` |\n| Comparison | `==`, `!=`, `\u003c`, `\u003c=`, `>`, `>=`, `is`, `is not` |\n| Logical | `and`, `or`, `not` |\n| Ternary | `x if condition else y` |\n| Built-in functions | `len()`, `max()`, `min()` |\n| Attribute access | `@outputs.result.field` |\n| Index access | `@variables.items[0]` |\n| String methods | `contains`, `startswith`, `endswith` |\n\n> ⚠️ **Portability warning**: `contains` / `startswith` / `endswith` may compile, but they are not the safest choice for control-flow-critical validation or intent routing. Use them only as light heuristics. For URL validation, cancellation detection, or anything that must deterministically gate behavior, prefer a Flow/Apex/classifier action that returns a normalized boolean or enum.\n\n**NOT Supported:**\n\n| Operation | Workaround |\n|-----------|-----------|\n| Multiplication (`*`) | Use Flow/Apex action |\n| Division (`/`) | Use Flow/Apex action |\n| Modulo (`%`) | Use Flow/Apex action |\n| String concatenation (`+` on strings) | Use `{!var1}{!var2}` template injection |\n| List slicing (`items[1:3]`) | Use Flow to extract sublist |\n| List comprehensions (`[x for x in ...]`) | Use Flow/Apex for list transformation |\n| Lambda expressions | Use Flow/Apex action |\n| `for`/`while` loops | Use topic loop pattern (re-entry) |\n| `import` statements | Not available (security sandbox) |\n| Empty list literal in expressions (`== []`, `= []`) | Use `len(@variables.list) == 0` for empty check; use temp empty var for reset (Issue 33) |\n\n### Apex Complex Type Notation\n\nWhen action inputs or outputs reference Apex inner classes, use the `@apexClassType` notation:\n\n```\n@apexClassType/c__OuterClass$InnerClass\n```\n\n| Component | Description | Example |\n|-----------|-------------|---------|\n| `@apexClassType/` | Required prefix | — |\n| `c__` | Default namespace (or your package namespace) | `c__`, `myns__` |\n| `OuterClass` | The containing Apex class | `OrderService` |\n| ` sf-ai-agentscript — Skillopedia | Inner class separator | — |\n| `InnerClass` | The inner class name | `LineItem` |\n\n**Example:**\n```yaml\nactions:\n process_order:\n inputs:\n line_items: list[object]\n complex_data_type_name: \"@apexClassType/c__OrderService$LineItem\"\n target: \"apex://OrderService\"\n```\n\n> **Note**: This notation is used in the `complex_data_type_name` field of action input/output definitions in Agentforce Assets, not in the `.agent` file directly.\n\n---\n\n## Current Limitations and Practical Workarounds\n\n| Limitation | What happens | Practical workaround |\n|------------|--------------|----------------------|\n| `set @variables.list = []` in runtime logic | Parse error (`[` not allowed in expression position) | Define a dedicated empty list variable and assign from it |\n| Nested object mutation such as `set @variables.profile.name = ...` | Mutation is not portable / may fail silently or parse incorrectly | Flatten state into scalar variables or shape structured data upstream in Flow/Apex |\n| Direct variable capture with `set @variables.x = ...` | No user input is captured | Expose `@utils.setVariables` (or another LLM-invoked action) and let the planner fill `...` |\n| Exact quoted field names in special cases | Raw `.agent` may be valid, but some visual editors can rewrite the field name during round-trip save/reformat | Treat the raw `.agent` file in source control as the source of truth and diff after builder edits |\n\n---\n\n## Common Pitfalls\n\n| Pitfall | Symptom | Fix |\n|---------|---------|-----|\n| Mixed tabs/spaces | `SyntaxError: cannot mix` | Use consistent indentation |\n| Invalid boolean | Type mismatch | Use `True`/`False` (capitalized) |\n| Spaces in variable names | Parse error | Use `snake_case` |\n| Mutable + linked | Conflicting modifiers | Choose one modifier |\n| Missing `source:` for linked | Variable empty | Add `source: @session.X` |\n| Missing `default_agent_user` | Internal error on deploy | Add valid Einstein Agent User |\n| `@inputs` in `set` directive | Unknown deploy error | Use `@utils.setVariables` to capture inputs separately, then reference via `@variables` |\n| `set @variables.X = ...` | Variable never fills from user input | Capture the value through an LLM-invoked action such as `@utils.setVariables`, then read `@variables.X` later |\n| Bare action name (no prefix) | Action not found / ignored | Always use `@actions.action_name` in `run`, templates, and instruction text |\n| `run @actions.X with param = ...` | Deterministic inline `run` never slot-fills the parameter | Capture the value first with an LLM-invoked action, then run deterministically using the saved variable |\n| `run @actions.X` for utility / delegation / unresolved action | `ACTION_NOT_IN_SCOPE`, action not found, or available-actions list excludes the name | `run @actions.X` resolves only to topic-level `actions:` that declare `target:`. Use direct `set` / `transition to` for deterministic utility behavior, or let `reasoning.actions:` invoke the utility. |\n| `set @variables.object.field = ...` or `set @variables.object[\"field\"] = ...` | Nested object updates fail or behave inconsistently | Use flattened scalar variables, or update the structure in Flow/Apex and return a fresh object |\n| Null check vs empty string | Wrong comparison for null | Use `is None` for null checks, `== \"\"` for empty strings — they are different |\n| `is_required` not enforced | Planner invokes action without required inputs | Use `available when @variables.X is not None` guard instead. `is_required` is a hint, not a gate. See Issue 26 |\n| Raw `@system_variables.user_input contains/startswith/endswith` routing | Cancellation/revision intent checks behave inconsistently | Use a Flow/Apex/classifier action to normalize the utterance into a boolean or enum, then branch on `@variables.X`. See Issue 42 |\n| `contains` / `startswith` / `endswith` as critical validation gates | URL or string guards compile but behave unevenly across authoring/runtime contexts | Prefer an upstream validation action that returns an explicit scalar like `is_valid_url: boolean`. |\n| `date` type in action I/O | Runtime error `'Date'` | Use `object` + `complex_data_type_name: \"lightning__dateType\"` in action I/O. `date` works fine for variables. See Issue 28 |\n| `== []` or `set = []` in expressions | Parse error (`[` not allowed) | Use `len(@variables.list) == 0` for empty check; use temp empty var for reset. See Issue 33 |\n| `is_displayable: True` on prompt outputs | Agent returns blank/empty response | Set `is_displayable: False` and let the reasoner synthesize the output. If the output should influence the response or routing, also set `is_used_by_planner: True`. See Issue 34 |\n| Structured output assigned directly to scalar variable | Comparisons fail or wrapper/object text leaks into state | Inspect the output schema and access a concrete property such as `.value` only when that field actually exists, or flatten the output in Flow/Apex first. |\n| Line breaks in topic `description:` | Script breaks with syntax error | Keep `description:` on a single line — no line breaks. See Issue 35 |\n| Variable name matches system context | \"Field is already mapped to a Context Variable\" | Avoid names like `Locale`, `Channel`, `Status`, `Origin` — use prefixed names like `customer_locale`. See Issue 36 |\n| `filter_from_agent` + `is_used_by_planner` on output | `InvalidFormatError` + cascading `ACTION_NOT_IN_SCOPE` | These are mutually exclusive. Use only `filter_from_agent: True`; remove `is_used_by_planner`. See [Issue 40](known-issues.md#issue-40-filter-planner-conflict). |\n| Lifecycle arithmetic on mutable number without null guard | Silent crash: `None + 1` → \"unexpected error\" | Add `if @variables.X is None: set @variables.X = 0` before arithmetic in `before_reasoning` / `after_reasoning`. See Issue 41 |\n\n### `@inputs` in `set` — Deploy-Breaking Anti-Pattern\n\n```yaml\n# ❌ WRONG — @inputs in set causes unknown error at deploy time\nverify: @actions.verify_customer\n with account_number=...\n set @variables.account_number = @inputs.account_number\n\n# ✅ CORRECT — use @utils.setVariables to capture input separately\ncollect_input: @utils.setVariables\n with account_number=...\nverify: @actions.verify_customer\n with [email protected]_number\n set @variables.customer_name = @outputs.customer_name\n```\n\n### Direct Variable Capture — Use `@utils.setVariables`, not `set ... = ...`\n\n```yaml\n# ❌ WRONG — direct slot-fill in deterministic instructions does not work\nreasoning:\n instructions: ->\n set @variables.member_email = ...\n run @actions.send_verification_code\n with email = @variables.member_email\n\n# ✅ CORRECT — let the LLM capture the value through a tool\nreasoning:\n instructions: ->\n if @variables.member_email == \"\":\n | Ask the user for their email address, then call {[email protected]_email}.\n actions:\n save_email: @utils.setVariables\n description: \"Save the user's email address\"\n with member_email = ...\n```\n\nOnce the variable is set, later deterministic logic can safely reference `@variables.member_email`.\n\n### Bare Action Names — Always Use `@actions.` Prefix\n\n```yaml\n# ❌ WRONG — bare names in run, templates, and instructions\nrun set_user_name\n| Use add_to_cart to add items.\n\n# ✅ CORRECT — always prefix with @actions.\nrun @actions.set_user_name\n| Use {[email protected]_to_cart} to add items.\n```\n\n### `is None` vs `== \"\"` — Different Checks\n\n```yaml\n# ❌ WRONG — checks for empty string, not null\nif @variables.data == \"\":\n | Data is missing.\n\n# ✅ CORRECT — checks for null/undefined\nif @variables.data is None:\n | Data has not been set.\n\n# ✅ CORRECT — checks for empty string\nif @variables.data == \"\":\n | Data is set but empty.\n```\n\n> Use `is None` when a variable may not have been set at all. Use `== \"\"` when checking for an explicitly empty string value.\n\n### `run @actions.X` Only Works for Topic-Level Target-Backed Actions\n\n`run @actions.X` resolves only against topic-level `actions:` definitions that declare a real `target:`. It does **not** work for reasoning-level utilities, and it is also the wrong tool for topic-level utilities / delegations that do not have `target:`.\n\n```yaml\n# ❌ WRONG — set_user_name is a reasoning-level utility, not a target-backed action\nrun @actions.set_user_name # ACTION_NOT_IN_SCOPE / action not found\n\nreasoning:\n actions:\n set_user_name: @utils.setVariables\n with user_name=...\n```\n\n```yaml\n# ❌ WRONG — go_help is topic-level, but it is still a utility transition (no target:)\nactions:\n go_help: @utils.transition to @topic.help\n\nreasoning:\n instructions: ->\n run @actions.go_help # not a valid deterministic run target\n```\n\n```yaml\n# ✅ CORRECT — use direct deterministic primitives for non-target utility behavior\nreasoning:\n instructions: ->\n set @variables.user_name = \"Pat\"\n transition to @topic.help\n```\n\n```yaml\n# ✅ CORRECT — use run only for a topic-level target-backed action definition\nactions:\n load_customer:\n target: \"flow://Load_Customer\"\n inputs:\n customer_id: string\n outputs:\n customer_name: string\n\nreasoning:\n instructions: ->\n run @actions.load_customer\n with customer_id = @variables.customer_id\n set @variables.customer_name = @outputs.customer_name\n```\n\n**Rule of thumb:** if you want deterministic execution, `run` is correct **only** when the referenced action is a topic-level definition with `target:`. Otherwise use direct `set` / `transition to`, or let `reasoning.actions:` expose the utility for LLM-driven invocation.\n\n---\n\n## Upcoming: Naming Changes (Forward-Looking)\n\n> **Roadmap signal**: Salesforce is moving toward renaming core Agent Script concepts. Both old and new names will be supported during a transition period, with the old names deprecated later.\n\n| Current Name | New Name | Notes |\n|-------------|----------|-------|\n| `topic` | `subagent` | Reflects the delegation/supervision model |\n| `action` | `tool` | Aligns with industry-standard LLM terminology |\n\n**No action required now** — current `topic:` and `actions:` syntax continues to work. This is informational for forward planning.\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":45832,"content_sha256":"6c1ee98d76eeb299553594be671e561a24747bcf50c77fd542ffb870b4c6f5ba"},{"filename":"references/testing-guide.md","content":"\u003c!-- Parent: sf-ai-agentscript/SKILL.md -->\n# Testing & Validation Guide\n\n> The 100-Turn Gauntlet: Automated Batch Testing with LLM-as-Judge Scoring\n\n---\n\n## Enterprise Testing Infrastructure\n\n### Testing Center Overview\n\nA centralized hub for batch-testing utterances without deactivating your agent.\n\n**Capabilities:**\n- ✅ Run tests against draft or committed versions\n- ✅ No need to deactivate the live agent\n- ✅ Execute up to 100 test cases simultaneously\n- ✅ Compare results across different script versions\n- ✅ Export test results for stakeholder review\n\n> 💡 Think of it as \"staging\" for your agent - test safely while production runs.\n\n---\n\n## The 5 Quality Metrics\n\n| Metric | Description | Scale |\n|--------|-------------|-------|\n| **Completeness** | Did the response include ALL necessary information? | 0-5 |\n| **Coherence** | Did the agent sound natural vs raw JSON output? | 0-5 |\n| **Topic Assertion** | Did the agent route to the correct topic? | Pass/Fail |\n| **Action Assertion** | Did the agent invoke the expected actions? | Pass/Fail |\n| **Combined Score** | Determines production readiness | Composite |\n\n> ⚠️ 100% assertions with 0% coherence = correct but robotic. Both matter.\n\n---\n\n## Coherence: The Difference\n\n| Coherence: 0/5 ❌ | Coherence: 5/5 ✅ |\n|-------------------|-------------------|\n| **USER:** \"I want a refund for order #123\" | **USER:** \"I want a refund for order #123\" |\n| **AGENT:** `{\"status\": \"success\", \"case_id\": \"5008000123\", \"refund_auth\": true}` | **AGENT:** Great news! Your refund has been approved. For your records, your case number is 5008000123. The refund should appear on your card within 3-5 business days. |\n| *Raw JSON dump - not conversational* | *Complete information in natural language* |\n\n---\n\n## LLM-as-Judge\n\nA \"Judge LLM\" evaluates the \"Agent LLM\" using criteria that match your use case.\n\n**Benefits:**\n- ✅ **Scalable**: Evaluate thousands of responses automatically\n- ✅ **Customizable**: Criteria match your specific use case\n- ✅ **Consistent**: Removes human reviewer variability\n- ✅ **Explainable**: Each score includes reasoning\n- ✅ **Iterative**: Refine criteria based on edge cases\n\n> 💡 The Judge LLM compares responses against your \"Golden Response\" definition.\n\n---\n\n## Batch Testing Workflow\n\n### Step 1: Prepare Test Cases\n\nCreate a set of test utterances covering:\n- Happy path scenarios\n- Edge cases\n- Error conditions\n- Multi-turn conversations\n\n### Step 2: Define Assertions\n\nFor each test case, specify:\n- **Expected Topic**: Which topic should be activated\n- **Expected Actions**: Which actions should be invoked\n- **Golden Response**: Ideal response pattern\n\n### Step 3: Run Batch\n\n```bash\n# Run agent tests (--api-name refers to an AiEvaluationDefinition, not the agent)\nsf agent test run --api-name MyTestDef --wait 10 -o TARGET_ORG --json\n```\n\n### Step 4: Analyze Results\n\n| Metric | Score | Status |\n|--------|-------|--------|\n| **TOPIC ASSERTION** | 100% | ✅ PASS |\n| **ACTION ASSERTION** | 100% | ✅ PASS |\n| **COMPLETENESS** | 100% | ✅ PASS |\n| **COHERENCE** | 0% | ❌ FAIL |\n\n---\n\n## The Coherence Collapse Problem\n\n### Symptom\n\nBatch test shows:\n- Topic assertions: 100% ✅\n- Action assertions: 100% ✅\n- Completeness: 100% ✅\n- Coherence: 0% ❌\n\n### Root Cause\n\nAgent returns raw action output instead of natural language response.\n\n```yaml\n# Problem: No instruction to format response\nactions:\n get_order: @actions.get_order_details\n set @variables.order_data = @outputs.data\n# LLM just dumps the data\n```\n\n### Fix\n\nAdd explicit formatting instructions:\n\n```yaml\ninstructions: ->\n run @actions.get_order_details\n set @variables.order_data = @outputs.data\n\n | Here are your order details:\n | - Order Number: {[email protected]_data.number}\n | - Status: {[email protected]_data.status}\n | - Estimated Delivery: {[email protected]_data.eta}\n |\n | Is there anything else I can help with?\n```\n\n---\n\n## Metadata Lifecycle\n\n> See [cli-guide.md](cli-guide.md#three-phase-lifecycle) for the Draft -> Commit -> Activate lifecycle.\n\n---\n\n## Test Case Design\n\n### Coverage Categories\n\n| Category | Examples |\n|----------|----------|\n| **Happy Path** | Standard refund request, order lookup |\n| **Edge Cases** | Empty cart, expired offers, boundary values |\n| **Error Handling** | Invalid input, service failures |\n| **Security** | Unauthorized access attempts |\n| **Multi-Turn** | Conversation continuity, context preservation |\n\n### Test Case Template\n\n```yaml\ntest_case:\n name: \"High-risk customer full refund\"\n utterance: \"I want a refund for order #12345\"\n context:\n customer_id: \"CUST_001\"\n churn_risk: 85\n expected:\n topic: \"refund_processor\"\n actions: [\"get_churn_score\", \"process_refund\"]\n response_contains: [\"approved\", \"full refund\"]\n assertions:\n topic_match: true\n action_sequence: true\n coherence_min: 4\n```\n\n---\n\n## Context Variable Injection in Tests\n\n> Context variables control agent behavior at the session level. Injecting them correctly in tests is critical for testing auth-gated flows, routing logic, and action execution.\n\n### YAML Test Spec Format\n\nContext variables in `sf agent test` YAML specs use **bare names** (not `$Context.` prefixed):\n\n```yaml\ntestCases:\n - name: \"Test post-auth routing\"\n utterance: \"I need to check my account\"\n contextVariables:\n - name: RoutableId # Bare name, NOT $Context.RoutableId\n value: \"0MwXXXXXXXXXXXX\" # Real MessagingSession ID\n - name: CaseId\n value: \"500XXXXXXXXXXXXX\"\n - name: Verified_Check # Bypass auth flow in tests\n value: \"true\"\n expectedTopic: Account_Management\n```\n\n### Key Patterns\n\n| Pattern | Value | Effect |\n|---------|-------|--------|\n| `RoutableId` | Real MessagingSession ID | Actions receive real `recordId` instead of topic internal name |\n| `CaseId` | Real Case ID | Flows that reference `$Context.CaseId` work correctly |\n| `Verified_Check=true` | String \"true\" | Unlocks auth-gated topics (bypasses verification flow in tests) |\n\n### conversationHistory for Multi-Turn Tests\n\nFor multi-turn test scenarios, the `conversationHistory` block provides prior conversation context. The `topic` field in conversation history resolves **local developer names** (no hash suffix needed):\n\n```yaml\ntestCases:\n - name: \"Follow-up after product help\"\n utterance: \"Actually, I want to leave feedback\"\n conversationHistory:\n - role: user\n content: \"My doorbell camera isn't working\"\n - role: assistant\n content: \"I can help with that. Let me look up your devices.\"\n topic: Product_Help # Local name, NOT Product_Help_16jXXX\n expectedTopic: Feedback\n```\n\n### Prefix Resolution\n\nBoth bare names and `$Context.` prefixed names work at runtime — the platform resolves both. Recommend `$Context.` prefix for clarity in documentation, but use bare names in YAML specs (the YAML parser expects bare names).\n\n> **Cross-reference**: For Evaluation API testing patterns with state injection, see the `sf-ai-agentforce-testing` skill.\n\n---\n\n## Validation Commands\n\n### Pre-Deployment Validation\n\n```bash\n# Validate authoring bundle syntax\nsf agent validate authoring-bundle --api-name MyAgent -o TARGET_ORG --json\n\n# Run agent tests\nsf agent test run --api-name MyTestDef --wait 10 -o TARGET_ORG --json\n```\n\n### Common Validation Errors\n\n| Error | Cause | Fix |\n|-------|-------|-----|\n| `Invalid default_agent_user` | User doesn't exist | Create Einstein Agent User |\n| `Topic not found` | Typo in transition | Check topic name spelling |\n| `Mixed indentation` | Tabs + spaces | Use consistent formatting |\n| `Action not callable` | Wrong protocol | Check target protocol |\n\n---\n\n## Continuous Testing\n\n### CI/CD Integration\n\n```yaml\n# Example GitHub Actions workflow\nname: Agent Testing\non: [push, pull_request]\njobs:\n test:\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v3\n - name: Validate Agent\n run: sf agent validate authoring-bundle --api-name MyAgent -o TARGET_ORG --json\n - name: Run Tests\n run: sf agent test run --api-name MyTestDef --wait 10 -o TARGET_ORG --json\n```\n\n### Test Automation Best Practices\n\n1. **Run validation on every commit**\n2. **Run full test suite before merge**\n3. **Monitor coherence scores over time**\n4. **Alert on assertion failures**\n5. **Track coverage by topic and action**\n\n---\n\n## Key Takeaways\n\n| # | Concept |\n|---|---------|\n| 1 | **Testing Center** - Run up to 100 tests simultaneously |\n| 2 | **5 Quality Metrics** - Completeness, Coherence, Topic/Action Assertions, Combined |\n| 3 | **LLM-as-Judge** - Automated scoring against golden responses |\n| 4 | **Coherence Matters** - 100% assertions with 0% coherence = technically correct but unusable |\n| 5 | **Three-Phase Lifecycle** - Draft (edit) → Commit (freeze) → Activate (live) |\n| 6 | **CI/CD Integration** - Automate validation and testing in deployment pipeline |\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":8967,"content_sha256":"45bb66a69bb267aaf6fd34e5cad15ba0028f793d87c620b7820faf185b7cfe02"},{"filename":"references/upstream-recipes-audit.md","content":"\u003c!-- Parent: sf-ai-agentscript/SKILL.md -->\n\n# Upstream Recipes Audit\n\n> Audit target: [`trailheadapps/agent-script-recipes`](https://github.com/trailheadapps/agent-script-recipes)\n>\n> Audited against upstream commit: `9c9017691db4a90bc73189371d7d540e1d9f077c` (Mar 2026)\n\nThis document explains how this skill uses TrailheadApps as an input source without treating it as the sole source of truth.\n\n## Audit Summary\n\n**Verdict:** useful and worth tracking, but not authoritative by itself.\n\nUse TrailheadApps for:\n- example structures\n- emerging syntax patterns\n- recipe-style action and topic composition\n- corroborating evidence when it matches official docs and downstream org behavior\n\nDo **not** use TrailheadApps alone as the final truth for:\n- Service Agent user requirements\n- publish-time permission behavior\n- `connection messaging:` / `connections:` syntax\n- lifecycle hook runtime guarantees\n- config field canonical naming\n\n## What Upstream Confirms\n\n| Upstream finding | Evidence | Our verdict | Action in this skill |\n|---|---|---|---|\n| `developer_name` is canonical | Commit `9aa5ee2` changed `.airules/AGENT_SCRIPT.md` from `agent_name` to `developer_name` | Confirmed | Keep `developer_name` as canonical |\n| Always use `@actions.` prefixes | Commit `b790a6f` added explicit guidance for `run`, templates, and instruction text | Confirmed | Keep validator/doc guidance |\n| `run @actions.X` does not work for reasoning-level utilities | Commit `25e6865` documents `run` resolving only against topic-level `actions:` with `target:` | Confirmed | Keep `instruction-resolution.md` and validator guidance |\n| Employee Agents are now the dominant recipe model | Commit `60ca531` switched recipes to `AgentforceEmployeeAgent` | Confirmed | Treat upstream as Employee-Agent-heavy |\n\n## Where Upstream Is Stale or Inconsistent\n\n| Upstream pattern | Evidence | Our verdict | Guidance |\n|---|---|---|---|\n| `connections:` plural wrapper | `.airules/AGENT_SCRIPT.md` still shows `connections:` top-level ordering; `future_recipes/escalationPatterns` still uses it | Stale / contradicts downstream validation | Prefer `connection messaging:` only |\n| Config `description:` shown as primary field | `.airules/AGENT_SCRIPT.md` config block and current recipes still use `description:` | Aligned with official/public usage | Prefer `description:` in public docs/examples; keep `agent_description:` compatibility in local tooling |\n| `before_reasoning` removed from rules but still appears in recipes | Main rules removed it, but `future_recipes/customerServiceAgent` still uses it | Internally inconsistent | Keep lifecycle-hook docs, but be cautious about runtime guarantees |\n| Service Agent coverage is weak | Current repo is almost entirely Employee-Agent examples and has effectively no real Service-Agent `.agent` corpus | Incomplete source for Service-Agent authoring | Prefer this skill's service-user and publish docs |\n\n## Specific Upstream References Worth Knowing\n\n### 1. `developer_name` migration\n- Commit: `9aa5ee2`\n- Meaning: upstream moved away from `agent_name` in `.airules/AGENT_SCRIPT.md`\n- Impact: aligns with this skill's canonical field choice\n\n### 2. Bare action name fix\n- Commit: `b790a6f`\n- Meaning: upstream now explicitly warns against bare action names and recommends `@actions.` references everywhere\n- Impact: supports this skill's validator rule\n\n### 3. `run @actions.X` scope clarification\n- Commit: `25e6865`\n- Meaning: `run` resolves against topic-level target-backed actions, not reasoning-level utilities like `@utils.setVariables`\n- Impact: supports this skill's instruction-resolution guidance\n\n### 4. Employee-Agent bias\n- Commit: `60ca531`\n- Meaning: recipes moved broadly toward Employee Agent examples\n- Impact: upstream is now less representative of Service Agent deployment realities\n\n## Maintenance Policy for This Skill\n\nWhen upstream and local evidence disagree, use this priority order:\n\n1. official Salesforce docs\n2. local `sf agent validate` / `preview` / `publish` behavior in real orgs\n3. this skill's TDD and field validation corpus\n4. upstream/community examples\n\n## Recommended Ongoing Audit Checklist\n\nWhen reviewing new TrailheadApps commits, specifically check for:\n- config field changes (`developer_name`, `description`, `agent_type`, and any compatibility behavior around `agent_description`)\n- lifecycle hook additions/removals\n- connection block syntax\n- target protocol examples (`flow://`, `apex://`, `prompt://`)\n- Service vs Employee agent assumptions\n- examples that contradict known publish/runtime behavior\n\n## Current Decision Log\n\n- **Adopted from upstream:** `developer_name`, public config `description:`, `@actions.` prefix guidance, `run` vs utility clarification\n- **Not adopted from upstream:** plural `connections:` wrapper, implicit Employee-Agent bias as general guidance\n- **Handled cautiously:** lifecycle hooks and `run` inside lifecycle hooks\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":4935,"content_sha256":"4dc7ea3dfa3a421557a7b7c84ec9e82880c88d8953af1d025555e02f1ed72b3b"},{"filename":"references/validator-rule-catalog.md","content":"# Agent Script Validator Rule Catalog\n\n> Source of truth for the automatic `.agent` write/edit validator at `hooks/scripts/agentscript-syntax-validator.py`.\n>\n> Validator output prefixes findings with stable rule IDs like `[ASV-STR-015]` so teams can search this catalog, commit messages, and incident notes consistently.\n\n## Severity Model\n\n| Severity | Meaning | Typical action |\n|---|---|---|\n| **Blocking** | High-confidence compiler, publish, or severe runtime-break pattern | Fix before preview/publish |\n| **Warning** | Portability, runtime, org-drift, or best-practice risk | Review before publish |\n| **Info / N/A** | Checklist-only status when a rule is not applicable | No action |\n\n## Rule Prefixes\n\n| Prefix | Area |\n|---|---|\n| `ASV-STR-*` | Core structure / parse safety |\n| `ASV-CFG-*` | Config / identity semantics |\n| `ASV-CON-*` | Connection routing rules |\n| `ASV-ORG-*` | Org-aware existence / permission checks |\n| `ASV-RUN-*` | Runtime / planner / publish gotchas |\n| `ASV-QLT-*` | Quality / maintainability heuristics |\n\n---\n\n## Structure Rules\n\n| Rule ID | Severity | Check | Encoded behavior |\n|---|---|---|---|\n| `ASV-STR-001` | Blocking | Indentation consistency | Errors when tabs and spaces are mixed in leading indentation. |\n| `ASV-STR-002` | Blocking | Boolean capitalization | Errors on lowercase `true` / `false` in assignment-style lines; requires `True` / `False`. |\n| `ASV-STR-003` | Blocking | Required top-level blocks | Requires `config`, `system`, and exactly one `start_agent`. |\n| `ASV-STR-004` | Blocking | Exactly one `start_agent` | Errors on zero or multiple `start_agent` blocks. |\n| `ASV-STR-005` | Blocking | Topic / start collision | Errors if a `start_agent` name collides with a `topic` API name. |\n| `ASV-STR-006` | Blocking | Naming rules | Enforces Salesforce-style API names: starts with letter, alnum/underscore only, no trailing/consecutive underscores, max 80 chars. |\n| `ASV-STR-007` | Blocking | Invalid `connections:` wrapper | Errors on top-level `connections:`; use `connection messaging:` etc. |\n| `ASV-STR-008` | Blocking | `mutable` + `linked` conflict | Errors if a variable line combines both modifiers. |\n| `ASV-STR-009` | Warning | Context-colliding variable names | Warns on names like `Locale`, `Channel`, `Status`, `Origin`. |\n| `ASV-STR-010` | Blocking | Undefined variable references | Errors on `@variables.X` references in executable lines when `X` is undeclared. |\n| `ASV-STR-011` | Blocking | Undefined topic references | Errors on `@topic.X` when `topic X:` / `start_agent X:` is missing. |\n| `ASV-STR-012` | Warning | Topic/start description formatting | Warns when `topic` / `start_agent` `description:` appears multiline or block-scalar based. |\n| `ASV-STR-013` | Warning | Lifecycle `instructions:` wrapper | Warns when `before_reasoning:` / `after_reasoning:` nests `instructions:` instead of direct content. |\n| `ASV-STR-014` | Blocking | Empty conditional bodies | Errors on `if` / `else` blocks that contain no executable body. |\n| `ASV-STR-015` | Blocking | `else if` unsupported | Errors on literal `else if`. |\n| `ASV-STR-016` | Blocking | Nested `if` unsupported | Errors when an `if`/`else` is nested inside another conditional block. |\n| `ASV-STR-017` | Blocking | Top-level `actions:` unsupported | Errors on root-level `actions:` blocks. |\n| `ASV-STR-018` | Blocking | Linked variable defaults | Errors when a `linked` variable declares `= ...`. |\n| `ASV-STR-019` | Blocking | Linked variable container types | Errors on `linked object` and `linked list[...]`. |\n| `ASV-STR-020` | Blocking | Ellipsis misuse | Errors when `...` is used outside slot-fill style `with param=...` bindings. |\n| `ASV-STR-021` | Blocking | Reserved variable / I/O field names | Errors when variables or action I/O fields use reserved names such as `description`, `label`, `is_required`, `is_displayable`, `is_used_by_planner`, `model`. |\n\n---\n\n## Config / Identity Rules\n\n| Rule ID | Severity | Check | Encoded behavior |\n|---|---|---|---|\n| `ASV-CFG-001` | Blocking | Config completeness | Requires an identifier (`developer_name` or legacy `agent_name`) and description (`description` or compatibility `agent_description`). |\n| `ASV-CFG-002` | Blocking / Warning | Agent type semantics | Errors when Service Agents omit `default_agent_user`, Employee Agents include it, or `agent_type` is invalid. Warns when `agent_type` is omitted but `default_agent_user` implies Service Agent fallback. |\n| `ASV-CFG-003` | Warning | Duplicate config description fields | Warns when both `description:` and `agent_description:` are defined in the same config block. |\n| `ASV-CFG-004` | Warning | `default_agent_user` inline comments | Warns when `default_agent_user:` contains trailing inline comments / extra text. |\n| `ASV-CFG-005` | Warning | Optional config field shape | Warns on malformed optional config fields such as non-boolean `enable_enhanced_event_logs` or empty `company` / `role` / `agent_version` / `user_locale`. |\n| `ASV-CFG-006` | Blocking | Topic system override syntax | Errors on topic-level shorthand `system: \"...\"`; enforces nested `system:` + `instructions:` shape. |\n\n---\n\n## Connection Rules\n\n| Rule ID | Severity | Check | Encoded behavior |\n|---|---|---|---|\n| `ASV-CON-001` | Blocking | Route property completeness | If any route property is used on `connection messaging:` / `voice:` / `web:`, all required route fields must be present: `outbound_route_type`, `outbound_route_name`, `escalation_message`. |\n| `ASV-CON-002` | Blocking | Route target prefix | Errors when `outbound_route_name` does not start with `flow://`. |\n| `ASV-CON-003` | Warning | Route type portability | Warns when `outbound_route_type` is not the TDD-validated `OmniChannelFlow`. |\n\n---\n\n## Org-Aware Rules\n\n| Rule ID | Severity | Check | Encoded behavior |\n|---|---|---|---|\n| `ASV-ORG-001` | Blocking / Warning | Service Agent user existence | Queries the resolved target org and verifies `default_agent_user` exists, is active, is not `AutomatedProcess`, and has profile `Einstein Agent User`. Warns if the org query cannot run. |\n| `ASV-ORG-002` | Warning | Required Service Agent assignments | Warns when the Service Agent user is missing a verified `AgentforceServiceAgentUser` assignment or expected `{AgentName}_Access` assignment for target-backed actions. |\n| `ASV-ORG-003` | Warning | Apex permission coverage | Warns when the custom `{AgentName}_Access` permission set lacks `SetupEntityAccess` / `\u003cclassAccesses>` for apex targets or cannot be verified. |\n| `ASV-ORG-004` | Blocking / Warning | Flow target readiness | Errors when `flow://` action targets are missing or inactive in the resolved org. Warns if the org query cannot run. |\n| `ASV-ORG-005` | Warning | Other target protocol review | Warns when target protocols cannot be permission-checked automatically. |\n| `ASV-ORG-006` | Blocking / Warning | Apex target existence | Errors when `apex://` targets do not exist in the resolved org. Warns if the org query cannot run. |\n| `ASV-ORG-007` | Blocking / Warning | Connection route Flow readiness | Errors when connection `outbound_route_name: flow://...` targets are missing or inactive in the resolved org. Warns if the org query cannot run. |\n\n---\n\n## Runtime / Publish Gotchas\n\n| Rule ID | Severity | Check | Encoded behavior |\n|---|---|---|---|\n| `ASV-RUN-001` | Blocking | Empty list literal gotchas | Errors on `== []`, `!= []`, and `set ... = []`. |\n| `ASV-RUN-002` | Warning | `@inputs` in `set` | Warns on `set @variables.x = @inputs.y`. |\n| `ASV-RUN-003` | Blocking | Bare `run` action names | Errors on `run foo`; requires `run @actions.foo`. |\n| `ASV-RUN-004` | Blocking | Invalid metadata on `@utils.transition` | Errors when transition utilities use target-action-only metadata such as `label`, `require_user_confirmation`, etc. |\n| `ASV-RUN-005` | Warning | Prompt output displayability | Warns on prompt/generatePromptResponse outputs with `is_displayable: True`. |\n| `ASV-RUN-006` | Warning | `date` in action I/O | Warns when action inputs/outputs use primitive `date`; recommends Lightning date object mapping. |\n| `ASV-RUN-007` | Warning | `is_required: True` planner gap | Warns when required inputs exist without an `available when` guard. |\n| `ASV-RUN-008` | Warning | Agent-type-specific messaging patterns | Warns on Messaging config for Employee Agents and related mismatches. |\n| `ASV-RUN-009` | Blocking | Pipe content in lifecycle hooks | Errors when `before_reasoning:` / `after_reasoning:` contains `|` prompt lines. |\n| `ASV-RUN-010` | Warning | Lifecycle `run` portability | Warns on `run @actions.X` inside lifecycle hooks. |\n| `ASV-RUN-011` | Blocking | Missing target action `outputs:` | Errors when a target-backed action definition omits `outputs:`. |\n| `ASV-RUN-012` | Warning | Multiple `available when` portability | Warns when an action declares more than one `available when` clause. |\n| `ASV-RUN-013` | Warning | `require_user_confirmation` runtime gap | Warns on `require_user_confirmation: True` because runtime dialogs are unreliable. |\n| `ASV-RUN-014` | Blocking | `run` resolves outside target-backed definition scope | Errors when `run @actions.X` points at a reasoning-only utility, a topic-level utility/delegation without `target:`, or an unresolved action name instead of a topic-level target-backed action definition. |\n| `ASV-RUN-015` | Warning | Welcome/error interpolation in quoted string form | Warns when a system welcome/error line appears to combine inline `{!...}` interpolation with plain string form instead of template/block form. |\n| `ASV-RUN-016` | Warning | Welcome/error folded-scalar guidance | Warns when system welcome/error messages use folded scalars (`>`); prefer literal block style (`|`) for dynamic or multiline message text. |\n| `ASV-RUN-017` | Warning | Service Agent `@context.*` sources | Warns on linked `@context.*` sources in Service Agent files because support is surface-dependent. |\n| `ASV-RUN-018` | Warning | Escalation fallback heuristic | Warns when `@utils.escalate` is present without an obvious fallback / latch pattern. |\n| `ASV-RUN-019` | Warning | Large-file parser risk | Warns when line/topic/action counts cross heuristic complexity thresholds. |\n| `ASV-RUN-020` | Blocking | Output `filter_from_agent` + `is_used_by_planner` conflict | Errors when an action output field declares both `filter_from_agent` and `is_used_by_planner`. These are mutually exclusive visibility controls; the combination causes `InvalidFormatError` and cascading `ACTION_NOT_IN_SCOPE` failures that make the entire action invisible to `before_reasoning` and `reasoning` blocks. |\n| `ASV-RUN-021` | Warning | Lifecycle arithmetic without null guard | Warns when `before_reasoning` or `after_reasoning` does arithmetic on a mutable number variable (e.g., `set @variables.X = @variables.X + 1`) without a preceding `if @variables.X is None:` guard. Mutable number variables can arrive as `None` at runtime via Eval API state injection or platform initialization bugs, causing silent `None + 1` crashes. |\n| `ASV-RUN-022` | Warning | Platform guardrail topic conflict | Warns when custom topics duplicate the platform's 3 built-in guardrail tools (`Inappropriate Content Guardrail`, `Prompt Injection Guardrail`, `Reverse Engineering Guardrail`). The platform auto-injects these tools into every topic's `EnabledToolsStep`. Custom topics with names containing `inappropriate`, `prompt_injection`, or `reverse_engineering` create 3+3 tool duplication in the LLM tool set, confusing the planner and causing \"unexpected error\" crashes. Recommended: remove custom guardrail topics and let the platform handle content moderation. |\n| `ASV-RUN-023` | Warning | Raw `user_input` string matching | Warns when deterministic guards use `contains` / `startswith` / `endswith` directly on `@system_variables.user_input`. Raw last-utterance text is not a stable intent signal; normalize it into a boolean or enum first. |\n| `ASV-RUN-024` | Warning | String-method guard portability | Warns when deterministic guards rely on `contains` / `startswith` / `endswith` for control-flow-critical validation. These operators may compile, but they are not portable enough to be the sole routing or validation gate. |\n| `ASV-RUN-025` | Warning | Hidden prompt outputs without planner visibility | Warns when a prompt target output is hidden from direct display (`is_displayable: False` or `filter_from_agent: True`) but is not planner-visible. This often leads to \"action ran but response is missing\" behavior. |\n| `ASV-RUN-026` | Warning | Structured output assigned directly to scalar variable | Warns when an action output declared as `object`/`list[...]` is assigned directly to a scalar `@variables.X`. The output likely needs property access (for example `.value` if present in the schema) or flattening upstream in Flow/Apex. |\n\n---\n\n## Quality / Maintainability Heuristics\n\n| Rule ID | Severity | Check | Encoded behavior |\n|---|---|---|---|\n| `ASV-QLT-001` | Warning | Transition naming convention | Warns when utility transitions do not use the recommended `go_to_` prefix. |\n| `ASV-QLT-002` | Warning | Duplicate action descriptions | Warns when sibling actions reuse near-identical descriptions, reducing planner clarity. |\n| `ASV-QLT-003` | Warning | Duplicate topic descriptions | Warns when topic/start descriptions are near-identical, reducing topic-selection clarity. |\n| `ASV-QLT-004` | Warning | Sensitive action guard suggestion | Warns when obviously sensitive target-backed actions lack `available when` guards. |\n| `ASV-QLT-005` | Warning | Post-action check ordering | Warns when status-completion checks appear after LLM prompt lines instead of before them. |\n\n---\n\n## Checklist Sections Emitted by the Validator\n\nThe hook groups findings into these report sections:\n\n1. **Structure**\n2. **Agent identity**\n3. **Connections**\n4. **Targets & permissions**\n5. **Runtime gotchas**\n6. **Quality**\n\nThis grouping is intentionally broader than the rule prefixes so operators can scan reports quickly without memorizing every ID.\n\n## Authoring Guidance\n\n- Treat **blocking** IDs as pre-publish must-fix items.\n- Add rule IDs to commit messages when fixing platform regressions, for example: `fix(agent): resolve ASV-RUN-011 missing outputs on flow action`.\n- When the validator emits a warning that is intentionally accepted, record the reason in code comments or PR notes using the rule ID.\n- Keep this catalog in sync with `hooks/scripts/agentscript-syntax-validator.py` whenever a rule is added, removed, or re-severitized.\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":14593,"content_sha256":"e52b7746a1d3612e6308d46e1839ffb805d4fde4167a109a10e9c8afdae21711"},{"filename":"references/validator-test-matrix.md","content":"# Validator Test Matrix\n\n> Snapshot generated against the current repository assets using `hooks/scripts/agentscript-syntax-validator.py` after the rule-catalog expansion.\n>\n> Goal: distinguish **clean examples**, **warning-only templates**, and **intentionally partial snippets** that are expected to fail a full-agent validator.\n\n## Command Used\n\n```bash\npython3 - \u003c\u003c'PY'\nfrom importlib.util import spec_from_file_location, module_from_spec\nfrom pathlib import Path\nspec=spec_from_file_location('v','skills/sf-ai-agentscript/hooks/scripts/agentscript-syntax-validator.py')\nmod=module_from_spec(spec)\nspec.loader.exec_module(mod)\nfor path in sorted(Path('skills/sf-ai-agentscript/assets').rglob('*.agent')):\n res=mod.AgentScriptValidator(path.read_text(), str(path)).validate()\n print(path, len(res['errors']), len(res['warnings']))\nPY\n```\n\n## Summary\n\n| Outcome | Count | Notes |\n|---|---:|---|\n| Clean (`0 blocking / 0 warnings`) | 1 | Fully self-contained example |\n| Warning-only (`0 blocking / 1+ warnings`) | 16 | Usually complete templates with demo placeholders, portability notes, or style suggestions |\n| Blocking (`1+ blocking`) | 13 | Mostly partial snippets by design; a small number are org-dependent or pattern examples that still need hardening |\n\n## Clean\n\n| File | Notes |\n|---|---|\n| `assets/agents/hello-world-employee.agent` | Clean pass in current repo/context. |\n\n## Warning-Only Examples / Templates\n\n| File | Dominant rules | Interpretation |\n|---|---|---|\n| `assets/agents/hello-world.agent` | `ASV-CFG-007` | Complete example; uses demo `default_agent_user`. |\n| `assets/agents/multi-topic.agent` | `ASV-CFG-007` | Complete template; placeholders intentionally unresolved. |\n| `assets/agents/production-faq.agent` | `ASV-CFG-007` | Valid example; remaining warning is the demo `default_agent_user`. |\n| `assets/agents/simple-qa.agent` | `ASV-CFG-007` | Complete template with unresolved placeholders. |\n| `assets/components/escalation-setup.agent` | `ASV-CFG-007`, `ASV-RUN-018` | Complete escalation template; placeholders remain unresolved, plus the connection-backed escalation fallback heuristic. |\n| `assets/deterministic-routing.agent` | `ASV-CFG-007` | Complete example; nested-`if` issue fixed during validator audit and inline demo-user comment removed. |\n| `assets/flow-action-lookup.agent` | `ASV-CFG-007` | Complete example with only the demo `default_agent_user` reminder remaining. |\n| `assets/hub-and-spoke.agent` | `ASV-CFG-007` | Complete example with only the demo `default_agent_user` reminder remaining. |\n| `assets/minimal-starter.agent` | `ASV-CFG-007` | Minimal starter is structurally valid; remaining warning is the demo `default_agent_user`. |\n| `assets/patterns/critical-input-collection.agent` | `ASV-RUN-012`, `ASV-RUN-007`, `ASV-CFG-007` | Complete pattern demonstrating planner-hint caveats; warnings are instructional. |\n| `assets/patterns/multi-step-workflow.agent` | `ASV-RUN-007`, `ASV-RUN-012`, `ASV-QLT-005` | Complete pattern; warnings are mostly planner-hint guidance plus one post-action ordering suggestion. |\n| `assets/patterns/procedural-instructions.agent` | `ASV-RUN-007`, `ASV-CFG-007` | Complete pattern with advisory warnings only. |\n| `assets/patterns/prompt-template-action.agent` | `ASV-RUN-007`, `ASV-CFG-007` | Complete pattern now uses the safer prompt-output flags; remaining warnings are planner-hint guidance plus demo placeholders. |\n| `assets/patterns/system-instruction-overrides.agent` | `ASV-RUN-010`, `ASV-CFG-007` | Complete pattern; lifecycle `run` warnings are expected portability cautions. |\n| `assets/prompt-rag-search.agent` | `ASV-CFG-007` | Complete example with only the demo `default_agent_user` reminder remaining. |\n| `assets/verification-gate.agent` | `ASV-CFG-007` | Complete pattern; undefined `refund_amount` bug was fixed during validator audit and inline demo-user comment removed. |\n\n## Blocking Files\n\n### A. Intentionally Partial Snippets\n\nThese are not complete agents and are expected to fail a full-agent validator because they are meant to be embedded into a larger file.\n\n| File | Dominant blockers |\n|---|---|\n| `assets/components/apex-action.agent` | `ASV-STR-003`, `ASV-CFG-001`, `ASV-STR-004` |\n| `assets/components/error-handling.agent` | `ASV-STR-003`, `ASV-CFG-001`, `ASV-STR-004` |\n| `assets/components/flow-action.agent` | `ASV-STR-003`, `ASV-CFG-001`, `ASV-STR-004` |\n| `assets/components/n-ary-conditions.agent` | `ASV-STR-003`, `ASV-CFG-001`, `ASV-STR-004` |\n| `assets/components/topic-with-actions.agent` | `ASV-STR-003`, `ASV-CFG-001`, `ASV-STR-004` |\n| `assets/patterns/action-callbacks.agent` | `ASV-STR-003`, `ASV-CFG-001`, `ASV-STR-004` |\n| `assets/patterns/advanced-input-bindings.agent` | `ASV-STR-003`, `ASV-CFG-001`, `ASV-STR-004` |\n| `assets/patterns/delegation-routing.agent` | `ASV-STR-003`, `ASV-CFG-001`, `ASV-STR-004` |\n| `assets/patterns/lifecycle-events.agent` | `ASV-STR-003`, `ASV-CFG-001`, `ASV-STR-004` |\n| `assets/patterns/llm-controlled-actions.agent` | `ASV-STR-003`, `ASV-CFG-001`, `ASV-STR-004` |\n| `assets/patterns/open-gate-routing.agent` | `ASV-STR-003`, `ASV-CFG-001` |\n| `assets/patterns/bidirectional-routing.agent` | `ASV-STR-003`, `ASV-CFG-001`, `ASV-STR-011` |\n\n### B. Context / Org-Dependent Blocking Cases\n\n| File | Dominant blockers | Interpretation |\n|---|---|---|\n| `assets/escalation-pattern.agent` | `ASV-ORG-007` | Structurally valid, but the referenced outbound route Flows do not exist in the currently resolved org, so the org-aware validator correctly blocks publish readiness. |\n\n## Notes from This Audit\n\n- The profile-aware harness now tracks both **expected blocking IDs** and **expected warning IDs**. That means remaining warning-heavy teaching templates are documented rather than treated as unexplained noise.\n- The validator expansion surfaced **real sample issues** that were worth fixing:\n - `assets/deterministic-routing.agent` was flattened to remove unsupported nested `if` blocks.\n - `assets/verification-gate.agent` now declares `refund_amount` before it is referenced.\n - `assets/components/escalation-setup.agent` no longer uses invalid `set @variables.x = ...` syntax.\n- The new `ASV-CFG-007` rule reduces noise on **template/demo values** by reporting them as unresolved placeholders/demo credentials instead of hard org failures.\n- The remaining blocking cohort is largely expected because many `assets/components/*` and `assets/patterns/*` files are **partial building blocks**, not standalone publishable agents.\n\n## Recommended Next Step\n\nIf you want the entire `assets/` tree to pass as a test suite, split it into two lanes:\n\n1. **standalone agents/templates** — must validate with `0 blocking`\n2. **partial snippets** — validate with a snippet-aware harness or a looser parser profile\n\nThat separation would let CI enforce publishability for complete examples without falsely failing the reference snippets.\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":6926,"content_sha256":"df04159c6a4d7476ecd8c56fa0aabbf65aa0b117fad2774b048624148e998421"},{"filename":"references/version-history.md","content":"\u003c!-- Parent: sf-ai-agentscript/SKILL.md -->\n\n# Version History\n\n| Version | Date | Changes |\n|---------|------|---------|\n| 2.10.0 | 2026-03-28 | **SF CLI 2.128.5 command refresh**: Updated Agent Script CLI guidance to reflect that `sf agent activate` / `sf agent deactivate` now support `--json`, documented the new `--version` flag for deterministic activation in CI/CD, and removed the invalid Agent Script packaging example that used `sf agent generate template` with `.agent` files. Added managed-packaging caveats pointing back to the current Agent Script limitation, refreshed activation examples across `SKILL.md`, `cli-guide.md`, `agent-user-setup.md`, and `customer-web-client.md`, and added explicit `BotVersion` retrieve examples for version-targeted workflows. |\n| 2.9.0 | 2026-03-20 | **Distilled deterministic-string and prompt-output guidance**: Added public, source-agnostic guidance that raw `@system_variables.user_input` substring checks are brittle for deterministic routing and that `contains` / `startswith` / `endswith` should not be the sole gate for control-flow-critical validation. Documented the safer prompt-template output pattern (`is_displayable: False` + `is_used_by_planner: True`) and clarified that missing prompt responses often require checking planner visibility separately from displayability. Added output-schema guidance explaining that structured outputs may require field access such as `.value`, but only when the declared schema actually exposes that field. Expanded the validator with new warning rules for raw `user_input` string matching, general string-method guard portability, prompt outputs hidden without planner visibility, and structured outputs assigned directly to scalar variables. Updated prompt-template teaching assets to use the safer output flags. |\n| 2.8.1 | 2026-03-19 | **Deterministic `run` scope clarification**: Tightened skill guidance so `run @actions.X` is documented as deterministic **only** when `X` resolves to a topic-level action definition with `target:`. Expanded `ASV-RUN-014` in `agentscript-syntax-validator.py` to block three real-world failure modes with one rule: (1) `run` against reasoning-only utilities like `@utils.setVariables`, (2) `run` against topic-level utilities/delegations without `target:` such as `@utils.transition` / `@topic.X`, and (3) `run` against unresolved action names. Error messaging now points authors toward direct `set` / `transition to` or LLM-invoked `reasoning.actions:` utilities, matching observed `ACTION_NOT_IN_SCOPE` builder failures. Updated `SKILL.md`, `README.md`, `instruction-resolution.md`, `actions-reference.md`, `syntax-reference.md`, and teaching asset `patterns/llm-controlled-actions.agent` so the deterministic-vs-LLM-directed distinction is explicit. |\n| 2.8.0 | 2026-03-13 | **Validator rule-catalog expansion**: Expanded `agentscript-syntax-validator.py` with stable `ASV-*` rule IDs across structure, config, connection, org-aware, runtime, and quality checks. Added new blockers for `else if`, nested `if`, empty conditional bodies, top-level `actions:`, linked-variable defaults/types, lifecycle `|` lines, missing target-action `outputs:`, connection route completeness, reasoning-only `run @actions.X`, and apex/connection-flow existence checks. Added new warnings for template/demo config values, multiple `available when`, `require_user_confirmation`, lifecycle `run` portability, prompt-template output displayability, large-file risk, duplicate descriptions, and sensitive-action guard suggestions. New docs: [validator-rule-catalog.md](validator-rule-catalog.md), [validator-test-matrix.md](validator-test-matrix.md), and [asset-validation-profiles.md](asset-validation-profiles.md). Added profile-aware asset harness via `assets/validation-profiles.json` + `scripts/validate-asset-profiles.py` so standalone examples, org fixtures, and partial snippets can be validated in separate lanes, with explicit allowlists for both expected blocking IDs and expected warning IDs. Asset fixes during audit: flattened nested conditionals in `deterministic-routing.agent` and `patterns/open-gate-routing.agent`, added missing `refund_amount` to `verification-gate.agent`, and removed invalid `set ... = ...` placeholder syntax from `components/escalation-setup.agent`. |\n| 2.7.0 | 2026-03-13 | **Field-validated insights (wave 2)**: 7 new known issues (33-39): empty list literal `[]` not supported in expressions, `is_displayable: True` causes blank response on prompt outputs, topic `description:` line breaks break script, variable names conflict with system context mappings, Draft-vs-Committed behavior divergence, object variables not editable in preview, `@context.*` not supported for NGA Service Agents (P0 accuracy fix). New patterns: `knowledge: citations_enabled` property documented, `lightning:isPII` flag added to action I/O properties, empty list comparison/reset workarounds. Added `is_displayable: True` blank response gotcha to production-gotchas.md. Updated `@context` documentation with Employee-Agent-only caveat across syntax-reference.md and SKILL.md. |\n| 2.6.0 | 2026-03-12 | **Field-validated insights integration**: 7 new known issues (26-32): `is_required` planner no-op, ConcurrentModificationException on partial I/O, `date` type runtime error, ANTLR parser limit for large agents, \"Failed to create planner session\" 500, escalation loops without availability check, citations not rendering in NGA agents. 8 new syntax patterns documented: `agent_label` config property, `visibility` variable property, `@knowledge.*` resource references (`citations_url`, `rag_feature_config_id`, `citations_enabled`), `@MessagingSession.*` and `@MessagingEndUser.*` linked variable sources, action `source` attribute for asset library, limited `run @actions.X` success observed in some `before_reasoning:` target-backed cases (not reliable enough for broad recommendation), upcoming \"topic→subagent\" / \"action→tool\" naming changes. 2 P0 accuracy gaps fixed: `is_required` is not enforced by planner (use `available when` guards), `date` type fails at runtime in action I/O (use `object` + `lightning__dateType`). Added escalation availability check pattern to fsm-architecture.md and production-gotchas.md. Updated Issue 15 cross-reference to Issue 27. |\n| 2.5.0 | 2026-03-11 | **Production learnings integration**: Fixed `list[object]` SObject collection mapping (`lightning__recordInfoType` not `lightning__textType`) in complex-data-types.md. Added Flow Type -> Agent Script mapping table. Added Boolean-to-String coercion hazard to production-gotchas.md. Added Flow version mismatch warning to cli-guide.md. New [metadata-lifecycle.md](metadata-lifecycle.md) — GenAiPlannerBundle versioning, Orphan-Then-Delete cleanup pattern, Flow version drift, shared Apex dependencies. Added Standard Platform Topics (Inappropriate_Content, Prompt_Injection, Reverse_Engineering, Ambiguous_Question) to fsm-architecture.md. Added Auth Gate Leak anti-pattern with fix strategies. Added Context Variable Injection patterns to testing-guide.md (YAML format, conversationHistory, Verified_Check bypass). Moved Issue 16 to Resolved, sanitized Issue 18, added Issues 21-22 (old bundles block Apex deletion, Flow version drift). Collapsed stale planner engine comparison in debugging-guide.md. Removed duplicate Three-Phase Lifecycle from testing-guide.md. Updated document map with metadata-lifecycle.md. |\n| 2.4.0 | 2026-03-07 | **Agent user setup & permission model**: New [agent-user-setup.md](agent-user-setup.md) reference — 6-step Service Agent provisioning workflow, Employee Agent path, auto-generated PS warning (ORM1 testing: `NextGen_*_Permissions` incomplete), custom PS XML template, end-to-end verification checklist, troubleshooting table. Updated SKILL.md (service vs employee distinction across 6 locations), cli-guide.md (PS verification queries), README.md (requirements table), known-issues.md (Issue 10 enhanced with agent-type context). |\n| 2.3.0 | 2026-02-20 | **CLI audit & line budget refactoring**: Fixed `--message` → `--utterance` in preview send examples (P0 bug — flag doesn't exist). Added `sf agent activate` / `sf agent deactivate` lifecycle commands. Added `--no-spec` flag for `generate authoring-bundle`. Added 12 new `generate agent-spec` flags (`--tone`, `--enrich-logs`, `--full-interview`, `--spec` for iterative refinement, `--prompt-template`, `--grounding-context`, `--force-overwrite`, `--company-website`, `--max-topics`, `--agent-user`, `--output-file`, `--company-description`). Added preview mode documentation (simulated vs live, `--use-live-actions`, `--apex-debug`, `--output-dir`, `--authoring-bundle`). Moved Minimal Working Example to references/minimal-examples.md. Compressed Deployment Checklist to pointer. Updated deployment lifecycle: Validate → Deploy → Publish → Activate → (Deactivate → Re-publish → Re-activate). Verified against SF CLI v2.123.1. |\n| 2.2.0 | 2026-02-17 | **TDD Validation v2.2.0 — Action metadata properties validated**: 2 new agents (Val_Action_Meta_Props, Val_IO_Meta_Props). Corrected \"Features NOT Valid\" table → \"Feature Validity by Context\" — properties ARE valid on action definitions with targets, only invalid on `@utils.transition`. Validated 9 properties: `label` (action/topic/I/O), `require_user_confirmation`, `include_in_progress_indicator`, `progress_indicator_message`, `is_required`, `is_user_input`, `is_displayable`, `is_used_by_planner`. Root cause: v1.3.0 only tested on `@utils.transition`, never on target-backed actions. Added \"Action I/O Metadata Properties\" section with complete property tables. Clarified reserved field names (reserved as variable names, valid as I/O metadata properties). Updated actions-reference.md (label property, Input Properties table, Output Properties expansion). Updated known-issues.md Issue 6 (compiles on target-backed actions). Updated VALIDATION.md Tier 4 + findings. Updated syntax-reference.md with Action Metadata Properties subsection. 24 validation agents total. |\n| 2.0.0 | 2026-02-16 | **Production learnings audit**: Fixed `\u003c>` operator references in 3 files. Fixed config block field names. Removed false `label:` claims. Added 3 new known issues (I/O schemas, connections block, EinsteinAgentApiChannel). Added Two-Level Action System section. |\n| 1.9.0 | 2026-02-14 | **TDD v1.9.0 (16/16 PASS)**: 3 new agents. TDD disproved `else:` + nested `if`. `\u003c>` not valid. Multiple `available when` IS valid. |\n| 1.8.0 | 2026-02-12 | **Gap analysis audit**: Added official-sources.md, known-issues.md, migration-guide.md. Verification Protocol + Self-Improvement. |\n| 1.7.0 | 2026-02-09 | **CRITICAL FIX**: `apex://` works directly, GenAiFunction NOT needed for Agent Script. Rewrote action sections. |\n| 1.6.0 | 2026-02-07 | Content migration from former sf-ai-agentforce-legacy. Created actions-reference.md (602 lines). |\n| 1.5.0 | 2026-02-06 | Action patterns & prompt template docs (from @kunello PR #20). |\n| 1.3.0 | 2026-01-20 | Lifecycle hooks validated. Features NOT Valid section (corrected in v2.2.0). |\n| 1.2.0 | 2026-01-20 | Gap analysis vs agent-script-recipes. Expanded Action Target Protocols. |\n| 1.1.0 | 2026-01-20 | \"Ultimate Guide\" tribal knowledge integration. |\n| 1.0.x | 2026-01 | Initial release through progressive testing validation. |\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":11377,"content_sha256":"98fa83578fd16902a5e0c287bb16a3a04961efdb0dcd638e577acf0cd8f07f0a"},{"filename":"scripts/validate-asset-profiles.py","content":"#!/usr/bin/env python3\n\"\"\"Validate Agent Script asset examples using profile-aware expectations.\n\nWhy this exists:\n- Some files under assets/ are complete standalone templates.\n- Some are reusable partial snippets that intentionally omit top-level blocks.\n- Some are structurally valid but depend on org-specific resources.\n\nThis harness runs the main Agent Script validator against every asset file, then\ninterprets results according to `assets/validation-profiles.json`.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport json\nfrom collections import defaultdict\nfrom importlib.util import module_from_spec, spec_from_file_location\nfrom pathlib import Path\nfrom typing import Dict, List, Tuple\n\nROOT = Path(__file__).resolve().parents[1]\nVALIDATOR_PATH = ROOT / \"hooks/scripts/agentscript-syntax-validator.py\"\nPROFILES_PATH = ROOT / \"assets/validation-profiles.json\"\n\n\ndef load_validator():\n spec = spec_from_file_location(\"agentscript_validator\", VALIDATOR_PATH)\n module = module_from_spec(spec)\n assert spec.loader is not None\n spec.loader.exec_module(module)\n return module.AgentScriptValidator\n\n\ndef extract_rule_id(message: str) -> str | None:\n if message.startswith(\"[\") and \"]\" in message:\n return message[1 : message.index(\"]\")]\n return None\n\n\ndef load_profiles() -> Dict:\n with PROFILES_PATH.open(\"r\", encoding=\"utf-8\") as handle:\n return json.load(handle)\n\n\ndef build_file_map(profiles_payload: Dict) -> Dict[str, Dict]:\n file_map: Dict[str, Dict] = {}\n for profile in profiles_payload.get(\"profiles\", []):\n for file_path in profile.get(\"files\", []):\n resolved = str(Path(file_path).resolve())\n if resolved in file_map:\n raise SystemExit(f\"Duplicate profile assignment for {file_path}\")\n file_map[resolved] = profile\n return file_map\n\n\ndef main() -> int:\n AgentScriptValidator = load_validator()\n profiles_payload = load_profiles()\n file_map = build_file_map(profiles_payload)\n\n asset_files = sorted((ROOT / \"assets\").rglob(\"*.agent\"))\n unprofiled = [str(path.resolve()) for path in asset_files if str(path.resolve()) not in file_map]\n if unprofiled:\n print(\"❌ Unprofiled asset files detected:\")\n for path in unprofiled:\n print(f\" - {path}\")\n return 1\n\n profile_stats: Dict[str, Dict[str, int]] = defaultdict(\n lambda: {\n \"files\": 0,\n \"blocking\": 0,\n \"warnings\": 0,\n \"unexpected_blocking\": 0,\n \"unexpected_warnings\": 0,\n }\n )\n unexpected_blockers: List[Tuple[str, str]] = []\n unexpected_warnings: List[Tuple[str, str]] = []\n\n for path in asset_files:\n profile = file_map[str(path.resolve())]\n allowed_blocking_ids = set(profile.get(\"allowBlockingIds\", []))\n allowed_warning_ids = set(profile.get(\"allowWarningIds\", []))\n\n validator = AgentScriptValidator(path.read_text(encoding=\"utf-8\"), str(path))\n result = validator.validate()\n\n profile_name = profile[\"name\"]\n profile_stats[profile_name][\"files\"] += 1\n profile_stats[profile_name][\"blocking\"] += len(result[\"errors\"])\n profile_stats[profile_name][\"warnings\"] += len(result[\"warnings\"])\n\n for _, _, message in result[\"errors\"]:\n rule_id = extract_rule_id(message)\n if rule_id not in allowed_blocking_ids:\n unexpected_blockers.append((str(path), message))\n profile_stats[profile_name][\"unexpected_blocking\"] += 1\n\n for _, _, message in result[\"warnings\"]:\n rule_id = extract_rule_id(message)\n if rule_id not in allowed_warning_ids:\n unexpected_warnings.append((str(path), message))\n profile_stats[profile_name][\"unexpected_warnings\"] += 1\n\n print(\"Asset validation profile summary\")\n print(\"-------------------------------\")\n for profile in profiles_payload.get(\"profiles\", []):\n stats = profile_stats[profile[\"name\"]]\n print(\n f\"- {profile['name']}: {stats['files']} files, \"\n f\"{stats['blocking']} raw blocking findings ({stats['unexpected_blocking']} unexpected), \"\n f\"{stats['warnings']} raw warnings ({stats['unexpected_warnings']} unexpected)\"\n )\n\n failed = False\n if unexpected_blockers:\n failed = True\n print(\"\\n❌ Unexpected blocking findings:\")\n for path, message in unexpected_blockers[:20]:\n print(f\"- {path}\\n {message}\")\n if len(unexpected_blockers) > 20:\n print(f\" ... and {len(unexpected_blockers) - 20} more\")\n\n if unexpected_warnings:\n failed = True\n print(\"\\n❌ Unexpected warnings:\")\n for path, message in unexpected_warnings[:20]:\n print(f\"- {path}\\n {message}\")\n if len(unexpected_warnings) > 20:\n print(f\" ... and {len(unexpected_warnings) - 20} more\")\n\n if failed:\n return 1\n\n print(\"\\n✅ All asset files matched their configured blocking and warning expectations.\")\n return 0\n\n\nif __name__ == \"__main__\":\n raise SystemExit(main())\n","content_type":"text/x-python; charset=utf-8","language":"python","size":5140,"content_sha256":"42d3cdc5e342d3b6135bffe1f57274383f8b4f1b060d4b561ef4fccb0d0ccccb"},{"filename":"VALIDATION.md","content":"# Validation History: sf-ai-agentscript\n\nThis file tracks the validation history of the sf-ai-agentscript skill. Validation agents are deployed to a test org to verify that documented patterns still work with current Salesforce releases.\n\n## Latest Validation\n\n| Status | Date | Version | Agents Deployed | Test Org |\n|--------|------|---------|-----------------|----------|\n| ✅ PASS | 2026-02-17 | v2.2.0 | 24/24 | AgentforceTesting |\n\n## Validation Agent Results\n\n### Tier 1: Original Agents (13) — Re-validated\n\n| Agent | Pattern Tested | Publish | Duration | Notes |\n|-------|----------------|---------|----------|-------|\n| Val_Minimal_Syntax | Core block structure | ✅ PASS | 16s | config, system, start_agent, topic blocks |\n| Val_Arithmetic_Ops | +/- operators | ✅ PASS | 11s | Addition and subtraction working |\n| Val_Comparison_Ops | Comparison operators | ✅ PASS | 12s | ==, !=, \u003c, \u003c=, >, >=, and, or, not |\n| Val_Variable_Scopes | @variables namespace | ✅ PASS | 9s | mutable string/number/boolean |\n| Val_Topic_Transitions | @utils.transition | ✅ PASS | 12s | Permanent handoffs between topics |\n| Val_Latch_Pattern | Boolean re-entry | ✅ PASS | 14s | Latch variable for topic re-entry |\n| Val_Loop_Guard | Iteration protection | ✅ PASS | 12s | Counter-based loop guard |\n| Val_Interpolation | Variable injection | ✅ PASS | 9s | {[email protected]} in strings |\n| Val_Action_Properties | Action property validity | ✅ PASS | 13s | NEGATIVE: confirms invalid properties don't work |\n| Val_Before_Reasoning | before_reasoning lifecycle | ✅ PASS | 9s | Direct content under block (no instructions: wrapper) |\n| Val_After_Reasoning | after_reasoning lifecycle | ✅ PASS | 9s | Direct content under block (no instructions: wrapper) |\n| Val_Label_Property | label: property | ✅ PASS | 9s | NEGATIVE: confirms label is NOT valid on @utils.transition (v2.2.0: IS valid on target-backed actions — see Val_Action_Meta_Props) |\n| Val_Always_Expect_Input | always_expect_input | ✅ PASS | 11s | NEGATIVE: confirms not implemented |\n\n### Tier 2: New Agents (3) — v1.9.0 Patterns\n\n| Agent | Pattern Tested | Publish | Duration | Notes |\n|-------|----------------|---------|----------|-------|\n| Val_Else_Nested_If | else: + nested if | ✅ PASS | 9s | NEGATIVE: else: with nested if does NOT compile (Approach 3 INVALID) |\n| Val_Step_Guard | Step counter re-entry guard | ✅ PASS | 14s | Step variable guards topic selector re-routing |\n| Val_Multiple_Available_When | Multiple available when clauses | ✅ PASS | 14s | POSITIVE: Multiple available when on same action IS valid |\n\n**Total Duration**: ~183s (16 agents — Tier 1+2 only)\n\n### Tier 3: Flow/Apex Integration Agents (6) — v2.1.0 Patterns\n\n| Agent | Pattern Tested | Expected | Actual | Notes |\n|-------|----------------|----------|--------|-------|\n| Val_Apex_Bare_Output | Bare @InvocableMethod output naming | ✅ PASS | ❌ FAIL | NEGATIVE FINDING: bare List\u003cString> without @InvocableVariable wrappers is INCOMPATIBLE |\n| Val_Datetime_IO | `datetime` type in action I/O | ✅ PASS | ✅ PASS | datetime maps to lightning__dateTimeStringType |\n| Val_Name_Mismatch | Wrong output name vs Apex field | ❌ FAIL | ❌ FAIL | Expected: \"invalid output 'wrong_name'\" — confirmed exact-match required |\n| Val_Partial_Output | Subset of target outputs declared | ✅ PASS | ✅ PASS | Can declare fewer outputs than the Apex class exposes |\n| Val_No_Outputs | Inputs-only action (no outputs block) | ❌ FAIL | ❌ FAIL | Expected: \"Internal Error\" — outputs block specifically required |\n| Val_Level1_Only | Level 1 definition without Level 2 invocation | ✅ PASS | ✅ PASS | Action definitions without @actions.X invocations are valid |\n\n**Apex Test Classes Deployed**: `BareOutputAction.cls` (bare List\u003cString> return), `DateTimeAction.cls` (DateTime with @InvocableVariable wrappers)\n\n### Tier 4: Action Metadata Properties Agents (2) — v2.2.0 Patterns\n\n| Agent | Pattern Tested | Expected | Actual | Notes |\n|-------|----------------|----------|--------|-------|\n| Val_Action_Meta_Props | Action-level metadata: label (action+topic+I/O), require_user_confirmation, include_in_progress_indicator, progress_indicator_message | ✅ PASS | ✅ PASS | All properties valid on target-backed actions (apex://TestApexAction) |\n| Val_IO_Meta_Props | I/O-level metadata: is_required, is_user_input, is_displayable, is_used_by_planner | ✅ PASS | ✅ PASS | All I/O properties valid on target-backed actions (apex://TestApexAction) |\n\n**Root Cause of v1.3.0 False Negatives**: `Val_Action_Properties` only tested properties on `@utils.transition` (utility actions without targets). These properties ARE valid on action definitions with `target:` (Level 1 actions). The skill incorrectly generalized \"not valid on transitions\" → \"not valid anywhere.\"\n\n## TDD Findings (v2.2.0)\n\n### Finding 10: `label:` is valid on action definitions, topics, and I/O fields ✅\n\n- **Pattern**: `label:` property on topic block, action definition, input fields, and output fields\n- **Result**: All compile and publish successfully on `apex://TestApexAction` target\n- **Impact**: Corrected \"Features NOT Valid\" table in SKILL.md → renamed to \"Feature Validity by Context\"\n- **Scope**: Valid on target-backed actions; still NOT valid on `@utils.transition` (Val_Action_Properties confirms)\n\n### Finding 11: `require_user_confirmation`, `include_in_progress_indicator`, `progress_indicator_message` valid on target-backed actions ✅\n\n- **Pattern**: All three properties on an action definition with `target: \"apex://TestApexAction\"`\n- **Result**: Compile and publish successfully\n- **Impact**: Updated Feature Validity table: valid on actions with targets, not on `@utils.transition`\n- **Note**: `require_user_confirmation` compiles but runtime confirmation dialog does not trigger (Issue 6 still OPEN)\n\n### Finding 12: `is_required` and `is_user_input` valid as input metadata properties ✅\n\n- **Pattern**: `is_required: True` and `is_user_input: True` on action input definitions\n- **Result**: Both compile and publish successfully\n- **Impact**: Added Input Properties table to actions-reference.md; clarified reserved field names in SKILL.md\n\n### Finding 13: `is_displayable` and `is_used_by_planner` valid as output metadata properties ✅\n\n- **Pattern**: `is_displayable: False` and `is_used_by_planner: True` on action output definitions\n- **Result**: Both compile and publish successfully\n- **Impact**: `is_displayable` confirmed as standard name for `filter_from_agent` (alias relationship documented)\n\n## TDD Findings (v2.1.0)\n\n### Finding 4: Bare `@InvocableMethod` is INCOMPATIBLE with Agent Script ❌\n\n- **Pattern**: Apex class returning `List\u003cString>` directly (no `@InvocableVariable` wrapper classes)\n- **Rounds Tested**: 3 iterations — input name `name` (invalid), input name `input` (invalid), no inputs block (Internal Error)\n- **Root Cause**: The framework requires `@InvocableVariable` annotations to discover bindable parameter names. Without wrapper classes, no I/O name can be matched.\n- **Impact**: Added \"Bare @InvocableMethod Pattern (NOT Compatible)\" warning to SKILL.md and actions-reference.md\n- **Rule**: Always use `@InvocableVariable` wrapper classes when targeting Apex from Agent Script\n\n### Finding 5: `datetime` type works in action I/O ✅\n\n- **Pattern**: `startDateTime: datetime` and `endDateTime: datetime` in action inputs/outputs targeting Apex `DateTime` params\n- **Result**: Publishes successfully; maps to `lightning__dateTimeStringType` in Lightning type system\n- **Impact**: Added `datetime` → `lightning__dateTimeStringType` to SKILL.md type mapping tables\n\n### Finding 6: I/O names must exactly match `@InvocableVariable` field names ❌\n\n- **Pattern**: Output name `wrong_name` when Apex class has `@InvocableVariable public String outputText`\n- **Error**: `\"invalid output 'wrong_name'\"` at server-side compilation\n- **Impact**: Added I/O Name Matching Rules section to SKILL.md and actions-reference.md with exact-match table\n\n### Finding 7: Partial output declaration is valid ✅\n\n- **Pattern**: Declaring only `outputText: string` when Apex class also has `inputText: string` as output\n- **Result**: Publishes successfully — subset of outputs is valid\n- **Impact**: Added \"Partial Output Pattern\" documentation to SKILL.md\n\n### Finding 8: `outputs:` block specifically required (not just \"I/O\") ❌\n\n- **Pattern**: Action with `inputs:` block but no `outputs:` block\n- **Error**: `\"Internal Error, try again later\"` at server-side compilation\n- **Impact**: Updated Issue 15 in known-issues.md — `outputs:` block is specifically required. `inputs:` alone is NOT sufficient.\n\n### Finding 9: Level 1 without Level 2 is valid ✅\n\n- **Pattern**: Action definition in topic `actions:` block with `target:`, `inputs:`, `outputs:` but NO `@actions.X` invocation in `reasoning.actions:`\n- **Result**: Publishes successfully — action definitions exist for the planner without explicit invocation\n- **Impact**: Added \"Level 1 Without Level 2 Is Valid\" note to SKILL.md Two-Level Action System section\n\n## TDD Findings (v1.9.0)\n\n### Finding 1: `else:` + nested `if` does NOT compile ❌\n\n- **Pattern**: `else:` block containing a nested `if` statement (SKILL.md Approach 3)\n- **Error**: `Unexpected 'if'` at the nested if line, cascading `Unexpected '@utils'`, `Missing colon`, `Invalid syntax`\n- **Impact**: SKILL.md Approach 3 must be removed or marked INVALID\n- **Workaround**: Use compound conditions (`if A and B:`) or sequential ifs\n- **Note**: Local LSP validator PASSES this pattern — only server-side compiler rejects it\n\n### Finding 2: `\u003c>` not-equal operator does NOT compile ❌\n\n- **Pattern**: `if @variables.x \u003c> \"value\":` using `\u003c>` as not-equal\n- **Error**: `Unexpected '>' 44:29` — parser does not recognize `\u003c>` as a token\n- **Impact**: Remove `\u003c>` from SKILL.md operator table; only `!=` is valid\n- **Workaround**: Use `!=` operator (already validated in Val_Comparison_Ops)\n\n### Finding 3: Multiple `available when` clauses ARE valid ✅\n\n- **Pattern**: Two `available when` clauses on the same action\n- **Result**: Compiles and publishes successfully\n- **Impact**: SKILL.md constraint \"One `available when` per action\" is WRONG — remove it\n- **Impact**: Common Issues entry \"Duplicate 'available when' clause\" is WRONG — remove it\n- **Impact**: multi-step-workflow template pattern is CORRECT as-is\n\n## Patterns Validated\n\nEach validation agent tests specific patterns documented in SKILL.md:\n\n1. **Val_Minimal_Syntax** → Core Syntax\n - `config` block with agent_name, agent_label, default_agent_user\n - `system` block with messages and instructions\n - `start_agent` block with reasoning and actions\n - `topic` block with description and reasoning\n\n2. **Val_Arithmetic_Ops** → Expression Operators\n - Addition: `@variables.counter + 1`\n - Subtraction: `@variables.counter - 1`\n - Note: `*`, `/`, `%` are NOT supported\n\n3. **Val_Comparison_Ops** → Expression Operators\n - Equality: `==`, not-equal: `!=`\n - Comparisons: `\u003c`, `\u003c=`, `>`, `>=`\n - Logical: `and`, `or`, `not`\n - Note: `\u003c>` is NOT valid (see Finding 2)\n\n4. **Val_Variable_Scopes** → Variable Namespaces\n - `mutable string` with default value\n - `mutable number` with default value\n - `mutable boolean` with default value\n - `set @variables.x = value` assignment\n\n5. **Val_Topic_Transitions** → Topic Transitions\n - `@utils.transition to @topic.X` (permanent handoff)\n - Multi-step topic chains\n\n6. **Val_Latch_Pattern** → Production Gotchas\n - Boolean flag initialization\n - Setting latch on entry\n - Checking latch in topic selector\n - Clearing latch on completion\n\n7. **Val_Loop_Guard** → Production Gotchas\n - Iteration counter pattern\n - `available when` guard clause\n - Exit condition on max iterations\n\n8. **Val_Interpolation** → Instruction Syntax\n - Basic interpolation: `{[email protected]}`\n - Multiple variables in string\n - Conditional interpolation: `{!value if condition else alt}`\n\n9. **Val_Action_Properties** → Action Properties on @utils.transition (NEGATIVE)\n - CONFIRMED NOT VALID on `@utils.transition`: require_user_confirmation, include_in_progress_indicator, output_instructions, progress_indicator_message\n - CONFIRMED VALID: description only (on @utils.transition)\n - **CORRECTED (v2.2.0)**: These properties ARE valid on action definitions with `target:` — see Val_Action_Meta_Props (#23)\n\n10. **Val_Before_Reasoning** → Lifecycle Hooks\n - Content directly under `before_reasoning:` block\n - NO `instructions:` wrapper required\n\n11. **Val_After_Reasoning** → Lifecycle Hooks\n - Content directly under `after_reasoning:` block\n - NO `instructions:` wrapper required\n\n12. **Val_Label_Property** → label: on @utils.transition (NEGATIVE, scope-limited)\n - CONFIRMED NOT VALID on `@utils.transition` actions (v1.3.0)\n - **CORRECTED (v2.2.0)**: `label:` IS valid on target-backed action definitions, topics, and I/O fields — see Val_Action_Meta_Props (#23)\n\n13. **Val_Always_Expect_Input** → Unimplemented Features (NEGATIVE)\n - CONFIRMED NOT VALID: `always_expect_input:` property\n\n14. **Val_Else_Nested_If** → if/else Nesting (NEGATIVE)\n - CONFIRMED NOT VALID: `else:` block with nested `if` statement\n - CONFIRMED VALID: Compound conditions and sequential ifs as alternatives\n\n15. **Val_Step_Guard** → Topic Re-Entry Protection\n - Step counter in `start_agent` guards topic selector\n - `workflow_active` boolean flag pattern\n - `available when` on step completion\n\n16. **Val_Multiple_Available_When** → Action Guards\n - CONFIRMED VALID: Multiple `available when` clauses on same action\n - DISPROVES: \"One available when per action\" constraint in SKILL.md\n\n17. **Val_Apex_Bare_Output** → Bare @InvocableMethod (NEGATIVE)\n - CONFIRMED NOT COMPATIBLE: Bare `List\u003cString>` return without `@InvocableVariable` wrappers\n - 3 rounds tested: input name `name` (invalid), `input` (invalid), no inputs (Internal Error)\n - Rule: Always use `@InvocableVariable` wrapper classes for Agent Script actions\n\n18. **Val_Datetime_IO** → DateTime Type in Action I/O\n - CONFIRMED VALID: `datetime` type in action inputs/outputs\n - Maps to `lightning__dateTimeStringType` in Lightning type system\n - Apex target: `DateTimeAction.cls` with `DateTime` wrapper params\n\n19. **Val_Name_Mismatch** → I/O Name Matching (NEGATIVE)\n - CONFIRMED: I/O names must exactly match `@InvocableVariable` field names\n - Output name `wrong_name` vs Apex field `outputText` → `\"invalid output 'wrong_name'\"`\n\n20. **Val_Partial_Output** → Partial Output Declaration\n - CONFIRMED VALID: Declaring subset of target outputs is acceptable\n - Declared `outputText` only from `TestApexAction` (which has both input and output wrappers)\n\n21. **Val_No_Outputs** → Inputs-Only Action (NEGATIVE)\n - CONFIRMED: `outputs:` block is specifically required for publish\n - Action with `inputs:` but no `outputs:` → `\"Internal Error, try again later\"`\n - Validates Issue 15 nuance: outputs specifically required, not just \"I/O\"\n\n22. **Val_Level1_Only** → Level 1 Without Level 2\n - CONFIRMED VALID: Action definition in topic `actions:` block without `@actions.X` in reasoning\n - Level 1 definitions (target + I/O) without Level 2 invocations publish successfully\n\n23. **Val_Action_Meta_Props** → Action Metadata Properties (POSITIVE)\n - CONFIRMED VALID: `label:` on action definitions, topics, and I/O fields\n - CONFIRMED VALID: `require_user_confirmation: True` on target-backed actions (runtime Issue 6)\n - CONFIRMED VALID: `include_in_progress_indicator: True` on target-backed actions\n - CONFIRMED VALID: `progress_indicator_message:` on target-backed actions (both flow:// and apex://)\n - CORRECTS v1.3.0: Val_Action_Properties only tested @utils.transition (limited property support)\n\n24. **Val_IO_Meta_Props** → I/O Metadata Properties (POSITIVE)\n - CONFIRMED VALID: `is_required: True` on action inputs\n - CONFIRMED VALID: `is_user_input: True` on action inputs (LLM extracts from conversation)\n - CONFIRMED VALID: `is_displayable: False` on action outputs (alias for `filter_from_agent`)\n - CONFIRMED VALID: `is_used_by_planner: True` on action outputs\n\n## Validation Command\n\n```bash\n# Navigate to validation directory\ncd sf-ai-agentscript/validation\n\n# Deploy metadata first\nsf project deploy start \\\n --source-dir validation-agents/force-app/main/default/aiAuthoringBundles \\\n --target-org AgentforceTesting --json\n\n# Publish each agent\nfor agent in Val_Minimal_Syntax Val_Arithmetic_Ops Val_Comparison_Ops Val_Variable_Scopes \\\n Val_Topic_Transitions Val_Latch_Pattern Val_Loop_Guard Val_Interpolation \\\n Val_Action_Properties Val_Before_Reasoning Val_After_Reasoning \\\n Val_Label_Property Val_Always_Expect_Input Val_Else_Nested_If \\\n Val_Step_Guard Val_Multiple_Available_When \\\n Val_Apex_Bare_Output Val_Datetime_IO Val_Name_Mismatch \\\n Val_Partial_Output Val_No_Outputs Val_Level1_Only \\\n Val_Action_Meta_Props Val_IO_Meta_Props; do\n sf agent publish authoring-bundle --api-name \"$agent\" --target-org AgentforceTesting --json\ndone\n```\n\n## Test Org Configuration\n\n| Property | Value |\n|----------|-------|\n| **Target Org Alias** | `AgentforceTesting` |\n| **Einstein Agent User** | `[email protected]` |\n| **API Version** | 65.0 |\n| **Instance URL** | `dak00000gdhgdeay-dev-ed.develop.my.salesforce.com` |\n\n## History\n\n| Date | Version | Status | Passed | Failed | Notes |\n|------|---------|--------|--------|--------|-------|\n| 2026-02-17 | v2.2.0 | ✅ PASS | 24/24 | 0 | 2 new action metadata agents (Val_Action_Meta_Props, Val_IO_Meta_Props). Findings: label valid on actions/topics/I/O, require_user_confirmation/include_in_progress_indicator/progress_indicator_message valid on target-backed actions, is_required/is_user_input valid on inputs, is_displayable/is_used_by_planner valid on outputs. Root cause: v1.3.0 only tested @utils.transition |\n| 2026-02-17 | v2.1.0 | ✅ PASS | 22/22 | 0 | 6 new Flow/Apex integration agents. Findings: bare @InvocableMethod INCOMPATIBLE, datetime type WORKS, I/O names must exact-match, partial outputs VALID, outputs block REQUIRED, Level 1 without Level 2 VALID |\n| 2026-02-14 | v1.9.0 | ✅ PASS | 16/16 | 0 | 3 new agents + re-validation against AgentforceTesting. Found: else+nested-if INVALID, \u003c> INVALID, multiple available-when VALID |\n| 2026-01-20 | v1.1.0 | ✅ PASS | 8/8 | 0 | Initial validation framework implementation (R6-Agentforce-SandboxFull) |\n\n## Next Validation Due\n\n**2026-03-19** (30 days from last validation)\n\n---\n\n## Troubleshooting\n\n### If Validation Fails\n\n1. **Check the error message** - Salesforce will indicate what syntax changed\n2. **Update SKILL.md** - Document the new constraint or syntax requirement\n3. **Fix the validation agent** - Update to use correct syntax\n4. **Re-run validation** - Ensure all agents pass again\n5. **Update this file** - Log the issue and resolution in History\n\n### Common Issues\n\n| Issue | Cause | Resolution |\n|-------|-------|------------|\n| `Nonexistent flag: --source-dir` | CLI version change | Use `sf agent publish authoring-bundle --api-name` instead |\n| `Unknown error` on publish | Usually successful | Check full JSON output for actual status |\n| `Default agent user not found` | Wrong org or user inactive | Query target org for Einstein Agent User |\n| `AgentCompilationError` on deploy | Server-side compiler stricter than LSP | Fix agent, redeploy. Note: LSP may pass patterns the server rejects |\n| `Unexpected 'if'` inside else: | else: + nested if not valid | Use compound conditions or sequential ifs |\n| `Unexpected '>'` in condition | `\u003c>` not-equal not valid | Use `!=` instead |\n| jq parse errors on `--json` output | sf CLI emits control chars | Pipe through `tr -d '\\000-\\037'` before jq |\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":19989,"content_sha256":"b50497d733acee01b707ac0de2b2147785502d0ca357b0b55c933c5afd10d2dd"}],"content_json":{"type":"doc","content":[{"type":"heading","attrs":{"level":1},"content":[{"text":"SF-AI-AgentScript Skill","type":"text"}]},{"type":"paragraph","content":[{"text":"Agent Script is the ","type":"text"},{"text":"code-first","type":"text","marks":[{"type":"strong"}]},{"text":" path for deterministic Agentforce agents. Use this skill when the user is authoring ","type":"text"},{"text":".agent","type":"text","marks":[{"type":"code_inline"}]},{"text":" files, building finite-state topic flows, or needs repeatable control over routing, variables, actions, and publish behavior.","type":"text"}]},{"type":"blockquote","content":[{"type":"paragraph","content":[{"text":"Start with the shortest guide first: ","type":"text"},{"text":"references/activation-checklist.md","type":"text","marks":[{"type":"link","attrs":{"href":"references/activation-checklist.md","title":null}}]}]},{"type":"paragraph","content":[{"text":"Migrating from the Builder UI? 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Important: agents should read /llm.txt, /llms.txt, or /.well-known/skills.json to discover the public Skillopedia API.