KRCA: An Efficient Root Cause Analysis System in Hyper-Scale Microservice Systems via Agentic AI
Source
arxiv.org
Author
Jiamin Jiang, Jingfei Feng, Yu Luo, Qingliang Zhang, Yongqian Su, Wenwei Gu, Shenglin Zhang, Tianyu Cui, Yao Wu, Jielong Huang, Nan Qi, Dan Pei
Date
Why it matters
Root-cause search across a hyper-scale microservice fleet is too large for an AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition → to brute-force. Narrowing by API drilldown, then a skeleton causal graph as a high-recall prior before agent verification, reaches 0.88 AC@1 on root-cause service localization in production.
Terms in this piece · Glossary
multi-agent — Using several AI agents on one problem — splitting work in parallel, checking each other, or filling different roles like planner and reviewer.
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
articleRoot-Cause Attribution Is a Search Problem: Continual Search for Long-Horizon Agent FailuresHarsh Raj, David Lee, Anas Mahmoud, Renxiong Wang, Razvan-Gabriel Dumitru, Chenguang Wang, Tong Zhao, Yunzhong He, Darvin Yi, Vipul Gupta
articleDoes Graph Structure Earn Its Place in Microservice Root-Cause Analysis? A Controlled Study on RCAEval, and What the Benchmark Was Really MeasuringImad Bulji\'c
articleEviRCA: Decoupling Evidence Extraction from Reasoning for Microservice Root-Cause AnalysisYuhao Wang, Zhen Qin, Xingliang Wang, Guochang Li, Weize Li, Shuiguang Deng