{
  "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": "PromptGenerationAgent",
    "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": 2,
    "agent.capabilities.count": 0
  },
  "artifacts": {
    "provenance": {
      "repository": "git+ssh://repo/core/agents/prompt_generation_agent.py",
      "commit_sha": "a1b2c3d4e5f6g7h8i9j0",
      "docstring_hash": "sha256:d8c4b12389a..."
    },
    "extracted_capabilities": [],
    "core_logic_signatures": [
      {
        "name": "__init__",
        "ast_hash": "sha256:788c0c9054b3fe3a",
        "loc": 5,
        "source": "def __init__(self, config: Dict[str, Any], kernel=None):\n        \"\"\"\n        Initializes the PromptGenerationAgent.\n        \"\"\"\n        super().__init__(config, kernel=kernel)"
      },
      {
        "name": "execute",
        "ast_hash": "sha256:76c85e30b0747ae2",
        "loc": 30,
        "source": "async def execute(self, user_query: str, **kwargs: Any) -> str:\n        \"\"\"\n        Generates a high-quality prompt from a user query.\n        \"\"\"\n        logging.info(\"Generating improved prompt...\")\n\n        prompt_template = f\"\"\"\nYou are a world-class financial analyst assistant. Your task is to take a user's query and transform it into a detailed, high-quality prompt for another AI agent.\n\nThe user's query is: \"{user_query}\"\n\nBased on this query, generate a new prompt that is:\n- More specific and detailed.\n- Includes context relevant to financial analysis.\n- Is structured to elicit a comprehensive and accurate response from a financial analysis agent.\n\"\"\"\n\n        if self.kernel:\n            try:\n                # Use Semantic Kernel to run this if available\n                # Assuming self.kernel has a method to invoke a prompt (SK v1 style)\n                # or we just return a placeholder indicating SK usage.\n                logging.info(\"Using Semantic Kernel to generate prompt.\")\n                # In a real implementation: result = await self.kernel.invoke_prompt(prompt_template)\n                return f\"Optimized Prompt for: {user_query} (Enhanced by AI)\"\n            except Exception as e:\n                logging.error(f\"Error using Kernel: {e}\")\n\n        # Fallback logic\n        return f\"Please analyze the following request in detail, considering all financial risks and market conditions: {user_query}\""
      }
    ]
  }
}