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Large language model (LLM) applications in cloud root cause analysis (RCA)have been actively explored recently. However, current methods are stillreliant on manual workflow settings and do not unleash LLMs' decision-makingand environment interaction capabilities. We present RCAgent, a tool-augmentedLLM autonomous agent framework for practical and privacy-aware industrial RCAusage. Running on an internally deployed model rather than GPT families,RCAgent is capable of free-form data collection and comprehensive analysis withtools. Our framework combines a variety of enhancements, including a uniqueSelf-Consistency for action trajectories, and a suite of methods for contextmanagement, stabilization, and importing domain knowledge. Our experiments showRCAgent's evident and consistent superiority over ReAct across all aspects ofRCA -- predicting root causes, solutions, evidence, and responsibilities -- andtasks covered or uncovered by current rules, as validated by both automatedmetrics and human evaluations. Furthermore, RCAgent has already been integratedinto the diagnosis and issue discovery workflow of the Real-time ComputePlatform for Apache Flink of Alibaba Cloud.

RCAgent: Cloud Root Cause Analysis by Autonomous Agents with Tool-Augmented Large Language Models