Dynamic Agent Generation for Self-Adaptive Root Cause Analysis
Résumé
Modern software systems increasingly rely on microservice architectures, which enhance modularity and resilience but produce vast amounts of heterogeneous observability data—logs, metrics, and traces—to ensure reliable operation and early failure detection. Performing Root Cause Analysis (RCA) on such data is challenging due to its scale, heterogeneity, and evolving structure, which hinder effective correlation and reasoning across modalities. Although recent studies have explored statistical techniques, graph-based models, and Large Language Models (LLMs)-based agents for RCA, most remain static and task-specific, lacking the adaptability and coordination required to handle evolving diagnostic contexts. This paper introduces a self-adaptive agent generation framework that leverages LLMs to dynamically compose and orchestrate adapted diagnostic agents at runtime according to each anomaly’s characteristics. Two main agents drive this process: a Parser Agent that interprets natural-language queries and builds structured task specifications, and an Executor Agent that adapts and coordinates adapted agents analyzing multimodal observability data through a shared memory space. Experiments on Nezha (fault-injected) and OpenRCA (real-world) datasets show up to 12% and 22% gains in diagnostic accuracy, confirming the framework’s effectiveness in adaptive reasoning, coordination, and interpretable root cause identification.
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