{
  "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": "QuantumPortfolioManagerAgent",
    "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": 4,
    "agent.capabilities.count": 0
  },
  "artifacts": {
    "provenance": {
      "repository": "git+ssh://repo/core/agents/quantum_portfolio_manager_agent.py",
      "commit_sha": "a1b2c3d4e5f6g7h8i9j0",
      "docstring_hash": "sha256:d8c4b12389a..."
    },
    "extracted_capabilities": [],
    "core_logic_signatures": [
      {
        "name": "__init__",
        "ast_hash": "sha256:1093e0e01e9071d7",
        "loc": 9,
        "source": "def __init__(self, config: Dict[str, Any], **kwargs):\n        \"\"\"\n        Initializes the QuantumPortfolioManagerAgent.\n        \"\"\"\n        super().__init__(config, **kwargs)\n        self.data_fetcher = DataFetcher()\n        self.bridge = QuantumMonteCarloBridge()\n        self.lookback_period = config.get(\"lookback_period\", \"1y\")\n        self.interval = config.get(\"interval\", \"1d\")"
      },
      {
        "name": "execute",
        "ast_hash": "sha256:8275bb09fada3305",
        "loc": 112,
        "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": "_format_output",
        "ast_hash": "sha256:1e42bd1373b89df6",
        "loc": 19,
        "source": "def _format_output(self, result: Dict[str, Any], query: str) -> AgentOutput:\n        allocation = result.get(\"optimized_allocation\", {})\n        energy = result.get(\"quantum_energy\")\n        method = result.get(\"method\")\n\n        answer = f\"Quantum Portfolio Optimization Results ({method}):\\n\"\n        answer += f\"Query: {query}\\n\\n\"\n        answer += \"Optimal Allocation:\\n\"\n        for asset, weight in allocation.items():\n            answer += f\"- {asset}: {weight*100:.2f}%\\n\"\n\n        answer += f\"\\nQuantum State Energy: {energy:.4f} (Lower is more stable)\"\n\n        return AgentOutput(\n            answer=answer,\n            sources=[\"SimulatedQuantumBridge\", \"YahooFinance\"],\n            confidence=0.9,\n            metadata=result\n        )"
      },
      {
        "name": "get_skill_schema",
        "ast_hash": "sha256:3b68e7d12545201a",
        "loc": 26,
        "source": "def get_skill_schema(self) -> Dict[str, Any]:\n        return {\n            \"name\": \"QuantumPortfolioManagerAgent\",\n            \"description\": \"Optimizes asset allocation using Quantum Approximate Optimization Algorithm (QAOA).\",\n            \"skills\": [\n                {\n                    \"name\": \"optimize_portfolio\",\n                    \"description\": \"Calculates optimal portfolio weights.\",\n                    \"parameters\": {\n                        \"type\": \"object\",\n                        \"properties\": {\n                            \"tickers\": {\n                                \"type\": \"array\",\n                                \"items\": {\"type\": \"string\"},\n                                \"description\": \"List of ticker symbols (e.g. ['AAPL', 'MSFT'])\"\n                            },\n                            \"lookback_period\": {\n                                \"type\": \"string\",\n                                \"description\": \"Historical data period (default '1y')\"\n                            }\n                        },\n                        \"required\": [\"tickers\"]\n                    }\n                }\n            ]\n        }"
      }
    ]
  }
}