# Adaptive Root Cause Localization for Microservice Systems with Multi-Agent Recursion-of-Thought

## TL;DR

This paper introduces RCLAgent, a multi-agent recursion-of-thought framework that improves root cause localization in complex microservice systems by mimicking human reasoning and leveraging large language models, achieving superior accuracy with fewer requests.

## Contribution

The paper presents a novel multi-agent recursion-of-thought approach for root cause localization, integrating data and reasoning strategies inspired by human analysis, and demonstrating its effectiveness over existing methods.

## Key findings

- RCLAgent outperforms state-of-the-art methods in localization accuracy.
- It requires only a single request to identify root causes.
- Experimental results show significant improvements in complex environments.

## Abstract

As contemporary microservice systems become increasingly popular and complex-often comprising hundreds or even thousands of fine-grained, interdependent subsystems-they are facing more frequent failures. Ensuring system reliability thus demands accurate root cause localization. While traces and metrics have proven to be effective data sources for this task, existing methods either heavily rely on pre-defined schemas, which struggle to adapt to evolving operational contexts, or lack interpretability in their reasoning process, thereby leaving Site Reliability Engineers (SREs) confused. In this paper, we conduct a comprehensive study on how SREs localize the root cause of failures, drawing insights from multiple professional SREs across different organizations. Our investigation reveals that human root cause analysis exhibits three key characteristics: recursiveness, multi-dimensional expansion, and cross-modal reasoning. Motivated by these findings, we introduce RCLAgent, an adaptive root cause localization method for microservice systems that leverages a multi-agent recursion-of-thought framework. RCLAgent employs a novel recursion-of-thought strategy to guide the LLM's reasoning process, effectively integrating data from multiple agents and tool-assisted analysis to accurately pinpoint the root cause. Experimental evaluations on various public datasets demonstrate that RCLAgent achieves superior performance by localizing the root cause using only a single request-outperforming state-of-the-art methods that depend on aggregating multiple requests. These results underscore the effectiveness of RCLAgent in enhancing the efficiency and precision of root cause localization in complex microservice environments.