Context-RCA: A Context-Controlled Multi-Agent Framework for Root Cause Analysis in Microservice Systems

Abstract

Root cause analysis (RCA) in microservice systems is challenging because observable symptoms often propagate along service dependencies and become separated from the actual underlying cause. Recent large language model (LLM)-based agent frameworks have shown promise for automating RCA, but existing methods still suffer from uncontrolled context growth, weakly constrained diagnostic workflows, and unreliable conclusions under inconsistent evidence from multiple sources. To address these issues, we propose Context-RCA, a context-controlled multi-agent framework for microservice fault diagnosis. Context-RCA has three key components. First, it uses selective context injection to organize agent inputs through domain isolation, stage awareness, and dynamic context updates. Second, it uses an SOP-driven workflow to enforce structured diagnosis across stages and prevent premature conclusions before sufficient evidence is collected. Third, it uses iterative consensus verification to cross-check hypotheses against logs, metrics, and traces and refine judgments from observed symptoms to deeper root causes. Experiments on 400 fault cases from the 2025 International AIOps Challenge dataset show that Context-RCA achieves an overall score of 65.93 on DeepSeek-V3.2 and 60.06 on GPT-4o, outperforming the strongest baseline by 14.21% and 24.30%, respectively. Context-RCA also improves fault localization and root cause description quality. Ablation results further show that selective context injection contributes a 5.51% gain, confirming context control as a primary factor in reliable RCA with LLMs.