Can LLMs autonomously manage microservices?
Enabling Autonomic Microservice Management through Self-Learning Agents
February 3, 2025
https://arxiv.org/pdf/2501.19056This paper introduces SERVICE ODYSSEY, a system for automating microservice management using self-learning agents powered by LLMs. It addresses the challenge of LLMs lacking specific service knowledge by enabling agents to learn autonomously through interaction with the microservice environment.
Key points for LLM-based multi-agent systems:
- Curriculum learning: Tasks are generated progressively, from simple observation to complex actions, allowing agents to gradually build understanding.
- Feedback-driven refinement: Solutions are refined iteratively based on environment, peer, and hierarchical feedback, improving execution and skill development.
- Knowledge curation: Successful solutions are consolidated into a skill library for future use, enabling continuous learning and adaptation.
- Agentic microservice system: Each microservice component is managed by an LLM-enhanced agent, facilitating natural language communication and control.
- Hierarchical agent architecture: A high-level manager decomposes tasks and coordinates low-level agents, enabling efficient collaboration.
- Practical application: The prototype demonstrates the system's effectiveness in a real-world microservice environment (Sock Shop) using GPT-4 and OpenAI 01.