Large language model (LLM) applications in cloud root cause analysis (RCA) have been actively explored recently. However, current methods are still reliant on manual workflow settings and do not unleash LLMs' decision-making and environment interaction capabilities. We present RCAgent, a tool-augmented LLM autonomous agent framework for practical and privacy-aware industrial RCA usage. Running on an internally deployed model rather than GPT families, RCAgent is capable of free-form data collection and comprehensive analysis with tools. Our framework combines a variety of enhancements, including a unique Self-Consistency for action trajectories, and a suite of methods for context management, stabilization, and importing domain knowledge. Our experiments show RCAgent's evident and consistent superiority over ReAct across all aspects of RCA -- predicting root causes, solutions, evidence, and responsibilities -- and tasks covered or uncovered by current rules, as validated by both automated metrics and human evaluations. Furthermore, RCAgent has already been integrated into the diagnosis and issue discovery workflow of the Real-time Compute Platform for Apache Flink of Alibaba Cloud.

翻译：近年来，大型语言模型（LLM）在云根因分析（RCA）中的应用得到了积极探索。然而，现有方法仍依赖于人工工作流设置，未能充分发挥LLM的决策与环境交互能力。本文提出RCAgent，一个工具增强的LLM自主代理框架，旨在实现实用且注重隐私的工业级RCA应用。RCAgent运行于内部部署模型而非GPT系列模型之上，能够借助工具进行自由形式的数据收集与综合分析。我们的框架融合了多种增强技术，包括独特的行动轨迹自我一致性机制，以及一套用于上下文管理、稳定化和领域知识导入的方法。实验表明，在自动指标与人工评估的双重验证下，RCAgent在RCA的各个方面——包括根因预测、解决方案、证据链与责任归属——以及当前规则覆盖或未覆盖的任务上，均显著且一致地优于ReAct方法。此外，RCAgent已成功集成至阿里云实时计算平台（Apache Flink）的诊断与问题发现工作流中。