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

Abstract

This paper details the design and implementation of an intelligent Operations and Maintenance (O&M) Agent system based on Large Language Models (LLM). The system adopts a multi-agent collaborative architecture, implementing automated O&M processes through an event-driven approach. The system integrates advanced AI capabilities to achieve core functionalities such as automated fault diagnosis, predictive maintenance, and knowledge accumulation.

I. O&M Agent Architecture Design

In designing the intelligent O&M Agent system, we adopted modular and event-driven architectural principles, breaking down complex O&M scenarios into independent capability domains, and achieving component decoupling and collaboration through a message bus.

1.1 Agent Capability Matrix

At the design stage, we decomposed O&M scenarios into five core capability domains, each managed by a specialized Agent:

Agent Type	Core Capabilities	Main Responsibilities
Monitoring Analysis Agent	Data Collection, Anomaly Detection	Responsible for system metric collection, alert generation, and preliminary analysis
Fault Diagnosis Agent	Root Cause Analysis, Solution Recommendation	Conducts multi-dimensional fault diagnosis, outputs solutions
Execution Operation Agent	Automated Repair, Resource Management	Executes repair operations, manages system resources
Decision Coordination Agent	Task Orchestration, Risk Control	Coordinates multiple Agent behaviors, controls execution risks
Knowledge Management Agent	Knowledge Base Maintenance, Experience Accumulation	Manages O&M knowledge, supports experience reuse

Each Agent has clear responsibility boundaries and capability definitions, interacting through standardized interfaces. This design ensures both the independence and maintainability of individual Agents while enabling collaboration for complex O&M scenarios.

1.2 System Architecture Design

The overall system adopts an event-driven microservice architecture, with core components including:

Core Component Description:

Message Bus: An event stream processing system based on Kafka, responsible for message transmission and event flow between Agents, ensuring system component decoupling and scalability.
Agent Scheduler: Responsible for Agent lifecycle management and task distribution, including core functions such as Agent creation, destruction, and load balancing, ensuring efficient utilization of system resources.
LLM Service: Provides intelligent analysis and decision-making capabilities, integrates large language models, and provides AI capability support such as natural language understanding and knowledge reasoning for various Agents.
Knowledge Base: An O&M knowledge storage based on vector database, storing historical cases, best practices, and other O&M knowledge, supporting similar case retrieval and knowledge reuse.
Execution Engine: Interfaces with infrastructure operation interfaces such as Kubernetes, responsible for converting Agent decisions into actual O&M operations, and ensuring execution safety and controllability.

1.3 Technology Stack Selection

The system's technology stack selection is based on the following levels:

Infrastructure Layer
- Container Orchestration: Using Kubernetes as the container orchestration platform, providing powerful container management and service orchestration capabilities
- Message Queue: Using Kafka for reliable event stream processing
- Data Storage: Using MongoDB for O&M data storage, Redis for high-performance cache support
Agent Framework Layer
- Development Language: Using Python 3.10+ as the main development language, leveraging its rich ecosystem
- Agent Framework: Using LangChain as the Agent development framework, simplifying AI capability integration
- LLM Model: Using GPT-4 as the core language model, providing powerful natural language understanding capabilities
O&M Tool Layer
- Monitoring System: Using Prometheus for system monitoring and metric collection
- Logging System: Using ELK Stack for log management and analysis
- Tracing System: Using Jaeger for distributed tracing, helping with problem location

II. Core Functionality Implementation

2.1 Monitoring Alert Processing

Monitoring alerts serve as the system's entry point, and we adopt a Prometheus + LLM combination solution:

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)
        }

2.2 Intelligent Fault Diagnosis

The fault diagnosis module uses RAG (Retrieval Augmented Generation) technology, combining historical cases with real-time data:

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

2.3 Automated O&M Process

Implemented automated O&M process based on K8s Operator:

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

3. System Optimization and Innovation

3.1 Knowledge Enhancement Mechanism

Implementing automatic updates and optimization of the knowledge base:

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']
            }
        )

3.2 Security and Controllability Assurance

Implementing multi-level security control mechanisms:

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

Summary and Future Outlook

Through practice, we have successfully built an efficient O&M Agent system that significantly improved operational efficiency:

Alert handling time reduced by 60%
Automated repair rate reached 75%
False positive rate reduced by 80%

In the future, we plan to continue optimization in the following areas:

Introduce more LLM capabilities to improve decision accuracy
Expand Agent collaboration mechanisms to support more complex O&M scenarios
Optimize knowledge base update mechanisms to improve knowledge reuse efficiency

We hope the practical experience shared in this article provides valuable reference for readers.

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