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Microservices pose challenges for automated fault resolution due to their distributed and complex nature. We present SysResolve, a framework that automates the entire resolution pipeline by combining multi-modal Root Cause Analysis (RCA) with Large Language Models (LLMs). RCA outputs are converted to natural language and passed through a Retrieval-Augmented Generation (RAG) pipeline to produce executable scripts. We evaluated and experimented on two microservices applications with three LLM (LlaMa3-70B, GPT-4, Claude 3.7). Our analysis highlights significant gains of current LLMs generation power from few-shot learning, with SysResolve achieving expert-level remediation while reducing recovery time.

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SysResolve: Study on In-Context LLM Generation of Resolution Scripts

  • 会议论文

  • 发表日期：

    2026年

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  • Harsh Borse,
  • Utkalika Satpathy,
  • Mainack Mondal,
  • Bivas Mitra

摘要

Microservices pose challenges for automated fault resolution due to their distributed and complex nature. We present SysResolve, a framework that automates the entire resolution pipeline by combining multi-modal Root Cause Analysis (RCA) with Large Language Models (LLMs). RCA outputs are converted to natural language and passed through a Retrieval-Augmented Generation (RAG) pipeline to produce executable scripts. We evaluated and experimented on two microservices applications with three LLM (LlaMa3-70B, GPT-4, Claude 3.7). Our analysis highlights significant gains of current LLMs generation power from few-shot learning, with SysResolve achieving expert-level remediation while reducing recovery time.