AI-Driven Microservice Governance Platform Based on Multi-Agent Architecture

This article introduces an AI-driven microservice governance solution using multi-agent architecture, designed to address the increasing complexity of microservice systems and higher operational requirements.

Background: Traditional software development relies heavily on human resources across requirements, development, testing, and deployment phases. The emergence of Large Language Models (LLMs) with code generation and chain-of-thought reasoning capabilities enables "intelligent emergence" in microservice development and operations, reshaping the entire software development lifecycle.

Key Challenges: The Jarvis platform built for commercial products faces issues including complex combinatorial operations with deep entry points and long chains, as well as high usage thresholds for advanced capabilities like root cause analysis and fault handling that heavily depend on human experience.

Solution Architecture: The platform adopts a multi-agent architecture with two key components: (1) Full-process conversational interaction - users issue instructions in natural language and complete complex operations like canary releases, rate limiting, and circuit breaking through multi-turn dialogues; (2) LLM reasoning and diagnosis - leveraging LLM's reasoning capabilities for root cause analysis, driving efficient fault handling through intelligent diagnosis and alerting mechanisms.

Agent Types: The system includes DirectorAgent (technical lead), CoderAgent (programmer), OpsAgent (operations), and DiagnosisAgent (diagnosis), all built on BaseAgent with SOP understanding and LLM ReAct chain-of-thought planning capabilities.

SOP-Based Collaboration: Standard Operating Procedures (SOPs) are encoded as agent prompts to guide LLMs through structured workflows, enabling domain-specific agents to validate outputs and reduce compound errors, effectively avoiding LLM hallucinations.

Data Flywheel: The system implements continuous evolution through large and small model collaborative evolution, using general-purpose LLMs for complex reasoning while lightweight models handle specific tasks through SFT fine-tuning. An offline AI dialogue evaluation system automatically assesses 40,000+ dialogue entries daily, driving rapid evolution from product, LUI technology parsing, and model foundation perspectives.