{
  "spec_version": "1.4.0-otel",
  "timestamp": "2026-04-24T00:13:03.355457Z",
  "trace_id": "4bf92f3577b34da6a3ce929d0e0e4736",
  "span_id": "00f067aa0ba902b7",
  "severity_text": "WARN",
  "severity_number": 13,
  "event_name": "swarm.agent.lifecycle.deprecated",
  "resource": {
    "service.name": "swarm-orchestrator",
    "service.version": "4.2.0",
    "deployment.environment": "production",
    "node.id": "worker-node-aws-us-east-1a-04"
  },
  "attributes": {
    "agent.id": "RegulatoryComplianceAgent",
    "agent.previous_version": "v29.x",
    "agent.target_version": "v30",
    "deprecation.reason": "Transitioning to v30 architecture.",
    "deprecation.action_required": true
  },
  "metrics": {
    "agent.core_logic.function_count": 10,
    "agent.capabilities.count": 7
  },
  "artifacts": {
    "provenance": {
      "repository": "git+ssh://repo/agents/regulatory.py",
      "commit_sha": "a1b2c3d4e5f6g7h8i9j0",
      "docstring_hash": "sha256:d8c4b12389a..."
    },
    "extracted_capabilities": [
      "Monitors regulatory changes and trends across relevant jurisdictions.",
      "Analyzes financial transactions and activities for compliance.",
      "Identifies potential regulatory risks and provides mitigation strategies.",
      "Generates compliance reports and audit trails.",
      "Collaborates with other agents to incorporate compliance considerations.",
      "Provides guidance on interacting with regulatory bodies.",
      "Adapts to changing political landscapes and regulatory priorities."
    ],
    "core_logic_signatures": [
      {
        "name": "__init__",
        "ast_hash": "sha256:8f4343466481234a",
        "loc": 14,
        "source": "def __init__(self, config: Dict[str, Any], **kwargs):\n        \"\"\"\n        Initializes the RegulatoryComplianceAgent with configuration parameters.\n\n        Args:\n            config: A dictionary containing configuration parameters.\n        \"\"\"\n        super().__init__(config, **kwargs)\n        self.regulatory_knowledge = self._load_regulatory_knowledge()\n        self.nlp_toolkit = self._initialize_nlp_toolkit()\n        self.political_landscape = self._load_political_landscape()\n        # Initialize LLM with config from agent config\n        self.llm = LLMPlugin(config=self.config.get(\"llm_config\"))\n\n        # Load learned parameters for continuous learning\n        self.learned_params = self._load_learned_profile()"
      },
      {
        "name": "_load_learned_profile",
        "ast_hash": "sha256:1a2b3c4d5e6f7g8h",
        "loc": 22,
        "source": "def _load_learned_profile(self) -> Dict:\n        \"\"\"\n        Loads the learned compliance profile (weights, risk modifiers) from disk.\n        \"\"\"\n        profile_path = \"data/compliance_learned_profile.json\"\n        default_profile = {\n            \"rule_weights\": {\n                \"threshold_violation\": 0.5,\n                \"geopolitical_risk\": 0.3\n            },\n            \"entity_risk_modifiers\": {},\n            \"learning_rate\": 0.05\n        }\n        if os.path.exists(profile_path):\n            try:\n                with open(profile_path, 'r') as f:\n                    return json.load(f)\n            except Exception as e:\n                logger.error(f\"Failed to load learned profile: {e}\")\n        return default_profile"
      },
      {
        "name": "_save_learned_profile",
        "ast_hash": "sha256:9h8g7f6e5d4c3b2a",
        "loc": 11,
        "source": "def _save_learned_profile(self):\n        \"\"\"\n        Saves the learned compliance profile to disk.\n        \"\"\"\n        profile_path = \"data/compliance_learned_profile.json\"\n        try:\n            os.makedirs(os.path.dirname(profile_path), exist_ok=True)\n            with open(profile_path, 'w') as f:\n                json.dump(self.learned_params, f, indent=2)\n        except Exception as e:\n            logger.error(f\"Failed to save learned profile: {e}\")"
      },
      {
        "name": "_load_regulatory_knowledge",
        "ast_hash": "sha256:5q6w7e8r9t0y1u2i",
        "loc": 33,
        "source_omitted_for_brevity": true,
        "note": "In a real telemetry pipeline, large functions can be truncated or omitted entirely in favor of the 'ast_hash' to save network bandwidth and storage costs."
      },
      {
        "name": "execute",
        "ast_hash": "sha256:z1x2c3v4b5n6m7l8",
        "loc": 47,
        "source_omitted_for_brevity": true
      }
    ]
  }
}