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April 5, 2026Computers, materials & continua/Computers, materials & continua (Print)2 citationsOpen Access

Graph-Augmented Multi-Agent Framework for Robust Root Cause Analysis in AIOps

Graph-Augmented Multi-Agent Robust Root Cause Analysis in AIOps

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Authors

HZHaodong ZouState Grid Corporation of China (China)YZYichen ZhaoShanghai Institute of Optics and Fine MechanicsXCXin ChenFuzhou University

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Overview

Novel framework enhances root cause analysis in complex systems, suggesting improved reliability and diagnostics.

Key Points

  • The aim is to develop an automated root cause analysis framework that effectively utilizes multi-modal observability data.
  • Develop a graph-augmented framework combining graph topology with large language models.
  • Engage in two phases: anomaly fusion graph construction and multi-agent collaborative reasoning.
  • Utilize a navigator agent to guide fault analysis and a verifier agent to reduce inaccuracies.
  • Achieved an average F1-score of 88.4%, outperforming existing methods by 4.6%.
  • Demonstrated comprehensive diagnostics by integrating multiple data modalities.
  • Proved effectiveness through extensive experiments across five diverse datasets.
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Cite This Study

Zou et al. (2026) studied this question.

synapsesocial.com/papers/69d1fd3da79560c99a0a3133https://doi.org/10.32604/cmc.2026.077908
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  4. 4Automating Root Cause Analysis: An Agentic Framework for Evidence-Led Reasoning over Distributed System Observability2026
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