{
  "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": 16,
    "agent.capabilities.count": 7
  },
  "artifacts": {
    "provenance": {
      "repository": "git+ssh://repo/core/agents/regulatory_compliance_agent.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": "sha256:8be20b4f0113d0fd",
        "loc": 16,
        "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:c30797667c7a657d",
        "loc": 20,
        "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:a5a683ac1835eb56",
        "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:a7cd2684c76260bd",
        "loc": 40,
        "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": "_initialize_nlp_toolkit",
        "ast_hash": "sha256:7cb510ded559e505",
        "loc": 17,
        "source": "def _initialize_nlp_toolkit(self) -> nltk.stem.WordNetLemmatizer:\n        \"\"\"\n        Initializes the NLP toolkit with necessary resources.\n\n        Returns:\n            An instance of the NLP toolkit.\n        \"\"\"\n        try:\n            # Download required NLTK resources\n            # Note: In production, these should be pre-downloaded\n            nltk.download('punkt', quiet=True)\n            nltk.download('wordnet', quiet=True)\n            nltk.download('vader_lexicon', quiet=True)  # For sentiment analysis\n        except Exception as e:\n            logger.warning(f\"NLTK download failed: {e}\")\n\n        return nltk.stem.WordNetLemmatizer()"
      },
      {
        "name": "_get_aml_updates",
        "ast_hash": "sha256:d9a22c18787b7d47",
        "loc": 23,
        "source": "def _get_aml_updates(self, api_key: str) -> List[str]:\n        \"\"\"\n        Retrieves AML updates from a regulatory API.\n\n        Args:\n            api_key: The API key for authentication.\n\n        Returns:\n            A list of AML updates.\n        \"\"\"\n        # Example API call (replace with actual API endpoint and parameters)\n        url = f\"https://regulatory-api.com/aml/updates?api_key={api_key}\"\n        try:\n            response = requests.get(url, timeout=10)\n            if response.status_code == 200:\n                updates = response.json().get(\"updates\", [])\n                return updates\n            else:\n                logger.warning(f\"Error retrieving AML updates: {response.status_code}\")\n                return []\n        except Exception as e:\n            logger.error(f\"Exception retrieving AML updates: {e}\")\n            return []"
      },
      {
        "name": "_load_political_landscape",
        "ast_hash": "sha256:9beab018f1f5991b",
        "loc": 21,
        "source": "def _load_political_landscape(self) -> Dict:\n        \"\"\"\n        Loads data on the current political landscape.\n\n        Returns:\n            A dictionary containing information on political leaders,\n            party affiliations, key policies, and recent developments.\n        \"\"\"\n        try:\n            loader = PoliticalLandscapeLoader()\n            landscape = loader.load_landscape()\n            return landscape\n        except Exception as e:\n            logger.error(f\"Error loading political landscape: {e}\")\n            # Return a minimal valid structure to prevent crashes\n            return {\n                \"US\": {\n                    \"president\": \"Unknown\",\n                    \"recent_developments\": [\"Data unavailable\"]\n                }\n            }"
      },
      {
        "name": "_analyze_transaction",
        "ast_hash": "sha256:ca8a7ddbcacc2d91",
        "loc": 74,
        "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": "_generate_compliance_report",
        "ast_hash": "sha256:e43665d06c07bd2a",
        "loc": 20,
        "source": "def _generate_compliance_report(self, transactions: List[Dict]) -> Tuple[str, List[Dict]]:\n        \"\"\"\n        Generates a compliance report for a list of transactions.\n\n        Args:\n            transactions: A list of dictionaries representing financial transactions.\n\n        Returns:\n            A tuple containing the compliance report string and the structured results list.\n        \"\"\"\n        report = \"Compliance Report:\\n\"\n        results = []\n        for transaction in transactions:\n            analysis = self._analyze_transaction(transaction)\n            results.append(analysis)\n            report += f\"  - Transaction {analysis['transaction_id']}: {analysis['compliance_status']}\\n\"\n            if analysis['violated_rules']:\n                report += f\"    Violated Rules: {', '.join(analysis['violated_rules'])}\\n\"\n            report += f\"    Risk Score: {analysis['risk_score']}\\n\"\n        return report, results"
      },
      {
        "name": "_get_regulatory_updates",
        "ast_hash": "sha256:774325de0180a362",
        "loc": 30,
        "source": "def _get_regulatory_updates(self) -> List[Tuple[str, str, str]]:\n        \"\"\"\n        Retrieves the latest regulatory updates from relevant sources.\n\n        Returns:\n            A list of tuples, where each tuple contains the source,\n            title, and summary of a regulatory update.\n        \"\"\"\n        updates = []\n\n        # 1. Scrape regulatory websites\n        # Example: Scraping FINRA website for KYC updates\n        finra_url = \"https://www.finra.org/rules-guidance/key-topics/know-your-customer\"\n        try:\n            response = requests.get(finra_url, timeout=10)\n            if response.status_code == 200:\n                finra_html = response.text\n                soup = BeautifulSoup(finra_html, 'html.parser')\n                # ... (Extract relevant updates from the parsed HTML)\n                # Example:\n                updates.append(\n                    (\"FINRA\", \"New KYC Rule\", \"Enhanced due diligence for high-risk customers.\")\n                )\n        except Exception as e:\n            logger.error(f\"Failed to scrape FINRA: {e}\")\n\n        # 2. Access regulatory RSS feeds\n        # ... (Implementation for accessing RSS feeds)\n\n        return updates"
      },
      {
        "name": "provide_guidance",
        "ast_hash": "sha256:e8993765b3505ae8",
        "loc": 27,
        "source": "def provide_guidance(self, user_question: str) -> str:\n        \"\"\"\n        Provides guidance on regulatory compliance matters using LLM.\n\n        Args:\n            user_question: The user's question regarding compliance.\n\n        Returns:\n            A string containing the guidance.\n        \"\"\"\n        prompt = (\n            \"You are a Regulatory Compliance Expert. Provide guidance on the following compliance matter.\\n\\n\"\n            f\"User Question: {user_question}\\n\\n\"\n            \"Context (Regulatory Knowledge):\\n\"\n            f\"{self.regulatory_knowledge}\\n\\n\"\n            \"Context (Political Landscape):\\n\"\n            f\"{self.political_landscape}\\n\\n\"\n            \"Guidance:\"\n        )\n\n        try:\n            guidance = self.llm.generate_text(prompt, task=\"regulatory_guidance\")\n            return guidance\n        except Exception as e:\n            # Fallback if LLM fails\n            logger.error(f\"LLM generation failed: {e}\")\n            return \"Please consult with a compliance expert for further assistance.\""
      },
      {
        "name": "_integrate_knowledge",
        "ast_hash": "sha256:ae61cc5a79a734d1",
        "loc": 23,
        "source": "def _integrate_knowledge(self, updates: List[Tuple[str, str, str]], knowledge_graph: Any = None):\n        \"\"\"\n        Integrates new regulatory updates into the knowledge base (Local + Graph).\n        \"\"\"\n        if not updates:\n            return\n\n        logger.info(f\"Integrating {len(updates)} regulatory updates into Knowledge Base.\")\n\n        # 1. Update local cache\n        for source, title, summary in updates:\n            if source not in self.regulatory_knowledge:\n                self.regulatory_knowledge[source] = {\"updates\": []}\n            self.regulatory_knowledge[source][\"updates\"].append({\"title\": title, \"summary\": summary})\n\n        # 2. Update UnifiedKnowledgeGraph if available\n        if knowledge_graph and hasattr(knowledge_graph, 'ingest_regulatory_data'):\n            formatted_updates = [{\"source\": s, \"title\": t, \"summary\": sum} for s, t, sum in updates]\n            try:\n                knowledge_graph.ingest_regulatory_data(formatted_updates)\n                logger.info(\"Updates ingested into UnifiedKnowledgeGraph.\")\n            except Exception as e:\n                logger.error(f\"Failed to ingest regulatory data into Graph: {e}\")"
      },
      {
        "name": "_continuous_learning",
        "ast_hash": "sha256:08180fd04d50cd11",
        "loc": 35,
        "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": "process_feedback",
        "ast_hash": "sha256:ced5273f0c7cbe2d",
        "loc": 28,
        "source": "def process_feedback(self, feedback_data: Dict[str, Any]):\n        \"\"\"\n        Processes feedback from audits or human reviewers to adjust learned parameters.\n\n        Args:\n            feedback_data: Dict containing 'transaction_id', 'correct_assessment' (bool),\n                           'rule_id' (optional), 'comment'.\n        \"\"\"\n        # This allows adjusting rule weights\n        if \"rule_id\" in feedback_data:\n            rule = feedback_data[\"rule_id\"]\n            correct = feedback_data.get(\"correct_assessment\", True)\n\n            weights = self.learned_params.setdefault(\"rule_weights\", {})\n            current_weight = weights.get(rule, 0.5)\n\n            learning_rate = self.learned_params.get(\"learning_rate\", 0.05)\n\n            if not correct:\n                # We were wrong (False Positive), decrease weight\n                weights[rule] = max(0.1, current_weight - learning_rate)\n                logger.info(f\"Feedback: Decreasing weight for rule {rule} to {weights[rule]}\")\n            else:\n                # We were right (True Positive), potentially increase (reinforcement)\n                weights[rule] = min(1.0, current_weight + (learning_rate * 0.1))\n                logger.info(f\"Feedback: Increasing weight for rule {rule} to {weights[rule]}\")\n\n        self._save_learned_profile()"
      },
      {
        "name": "_save_audit_trail",
        "ast_hash": "sha256:f21c15cdc4700185",
        "loc": 18,
        "source": "def _save_audit_trail(self, results: List[Dict]):\n        \"\"\"\n        Saves analysis results to a secure audit log file (JSONL).\n        \"\"\"\n        audit_file = \"logs/compliance_audit.jsonl\"\n        try:\n            os.makedirs(os.path.dirname(audit_file), exist_ok=True)\n            with open(audit_file, \"a\") as f:\n                for res in results:\n                    entry = {\n                        \"timestamp\": datetime.utcnow().isoformat(),\n                        \"agent_id\": self.config.get(\"agent_id\", \"unknown\"),\n                        **res\n                    }\n                    f.write(json.dumps(entry) + \"\\n\")\n            logger.info(f\"Audit trail saved to {audit_file}\")\n        except Exception as e:\n            logger.error(f\"Failed to save audit trail: {e}\")"
      },
      {
        "name": "execute",
        "ast_hash": "sha256:694ec988823b1a46",
        "loc": 65,
        "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."
      }
    ]
  }
}