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

As microservice systems become increasingly complex, traditional operational approaches face significant challenges. This article presents an AI-driven microservice governance solution using a multi-agent architecture that integrates operational expertise through natural language interaction and intelligent reasoning.

Background: Commercial advertising products require extensive manual investment for microservice operations to ensure system stability and rapid feature delivery. These operations include routine deployment, configuration changes, and SRE architecture optimization for root cause analysis and fault handling.

Key Innovation: The Jarvis platform adopts an AI-native approach with two core capabilities:

1. Full-Process Conversational Interaction: Users issue instructions in natural language, and multi-turn dialogue completes complex operations like grayscale releases, rate limiting, and circuit breaker configuration.

2. LLM Reasoning and Diagnosis: Leveraging large language models' reasoning capabilities for root cause analysis, intelligent diagnosis, and alarm-driven fault handling, enabling replication of human operational experience.

Architecture: The system uses a multi-agent framework where different agents (DirectorAgent, CoderAgent, OpsAgent, DiagnosisAgent) collaborate through standardized operating procedures (SOPs). Each agent has specific roles: DirectorAgent plans workflows, CoderAgent generates code from requirements, OpsAgent handles deployment, and DiagnosisAgent performs fault analysis.

Data Flywheel: The platform implements continuous evolution through a data flywheel mechanism that trains expert models and automatically extracts SOPs. It uses large models to teach smaller models, creating high-quality training datasets for fine-tuning lightweight models that balance intelligence and cost efficiency.