Design and Implementation of LLM-based Intelligent O&M Agent System

class AlertProcessor:
    def __init__(self):
        self.prom_client = PrometheusClient()
        self.llm_client = LLMClient()
        self.alert_rules = self._load_alert_rules()

    async def process_alert(self, alert: Alert) -> AnalysisResult:
        # 1. Get alert context
        context = await self._get_alert_context(alert)

        # 2. LLM analysis
        analysis = await self.llm_client.analyze(
            prompt=self._generate_prompt(alert, context),
            temperature=0.3
        )

        # 3. Result processing
        return self._process_analysis_result(analysis)

    async def _get_alert_context(self, alert: Alert) -> dict:
        # Get related metric data
        metrics = await self.prom_client.query_range(
            query=alert.metric_query,
            start=alert.start_time - timedelta(minutes=30),
            end=alert.start_time
        )

        # Get related logs
        logs = await self.log_client.query(
            service=alert.service,
            time_range=(alert.start_time - timedelta(minutes=5), alert.start_time)
        )

        return {
            "metrics": metrics,
            "logs": logs,
            "service_info": await self._get_service_info(alert.service)
        }

class DiagnosticAgent:
    def __init__(self):
        self.vector_store = VectorStore()  # Vector database client
        self.llm = LLMClient()            # LLM client

    async def diagnose(self, incident: Incident) -> DiagnosisResult:
        # 1. Retrieve related cases
        similar_cases = await self.vector_store.search(
            query=incident.description,
            filter={
                "service": incident.service,
                "severity": incident.severity
            },
            limit=5
        )

        # 2. Generate diagnostic solution
        diagnosis = await self.llm.generate(
            system_prompt=DIAGNOSTIC_SYSTEM_PROMPT,
            user_prompt=self._build_diagnostic_prompt(
                incident=incident,
                similar_cases=similar_cases
            )
        )

        # 3. Solution validation
        validated_result = await self._validate_diagnosis(diagnosis)

        return validated_result

class AutomationOperator:
    def __init__(self):
        self.k8s_client = kubernetes.client.CustomObjectsApi()
        self.risk_evaluator = RiskEvaluator()

    async def execute_action(self, action: Action) -> ExecutionResult:
        # 1. Risk assessment
        risk_level = await self.risk_evaluator.evaluate(action)
        if risk_level > RiskLevel.MEDIUM:
            return await self._handle_high_risk(action)

        # 2. Execute operation
        try:
            result = await self._execute(action)

            # 3. Verify result
            verified = await self._verify_execution(action, result)

            # 4. Update status
            await self._update_status(action, result, verified)

            return ExecutionResult(
                success=verified,
                action=action,
                result=result
            )

        except Exception as e:
            await self._handle_execution_error(action, e)
            raise

class KnowledgeBase:
    def __init__(self):
        self.vector_store = VectorStore()
        self.llm = LLMClient()

    async def update_knowledge(self, case: dict):
        # 1. Extract key information
        extracted_info = await self.llm.extract_key_info(case)

        # 2. Generate vector representation
        embeddings = await self._generate_embeddings(extracted_info)

        # 3. Update knowledge base
        await self.vector_store.upsert(
            id=case['id'],
            vector=embeddings,
            metadata={
                "type": case['type'],
                "service": case['service'],
                "solution": case['solution'],
                "effectiveness": case['effectiveness_score']
            }
        )

from enum import Enum
from typing import Optional

class RiskLevel(Enum):
    LOW = 1     # Read-only operations
    MEDIUM = 2  # Reversible operations
    HIGH = 3    # Irreversible operations
    CRITICAL = 4 # Critical operations

class SecurityController:
    def __init__(self):
        self.risk_evaluator = RiskEvaluator()
        self.audit_logger = AuditLogger()

    async def validate_operation(self, operation: dict) -> bool:
        # 1. Risk assessment
        risk_level = await self.risk_evaluator.evaluate(operation)

        # 2. Permission check
        if not await self._check_permissions(operation, risk_level):
            return False

        # 3. Audit logging
        await self.audit_logger.log_operation(operation, risk_level)

        # 4. Human approval (if needed)
        if risk_level >= RiskLevel.HIGH:
            return await self._require_human_approval(operation)

        return True