  The escalating complexity of micro-services architecture in cloud-native
technologies poses significant challenges for maintaining system stability and
efficiency. To conduct root cause analysis (RCA) and resolution of alert
events, we propose a pioneering framework, multi-Agent Blockchain-inspired
Collaboration for root cause analysis in micro-services architecture (mABC), to
revolutionize the AI for IT operations (AIOps) domain, where multiple agents
based on the powerful large language models (LLMs) perform blockchain-inspired
voting to reach a final agreement following a standardized process for
processing tasks and queries provided by Agent Workflow. Specifically, seven
specialized agents derived from Agent Workflow each provide valuable insights
towards root cause analysis based on their expertise and the intrinsic software
knowledge of LLMs collaborating within a decentralized chain. To avoid
potential instability issues in LLMs and fully leverage the transparent and
egalitarian advantages inherent in a decentralized structure, mABC adopts a
decision-making process inspired by blockchain governance principles while
considering the contribution index and expertise index of each agent.
Experimental results on the public benchmark AIOps challenge dataset and our
created train-ticket dataset demonstrate superior performance in accurately
identifying root causes and formulating effective solutions, compared to
previous strong baselines. The ablation study further highlights the
significance of each component within mABC, with Agent Workflow, multi-agent,
and blockchain-inspired voting being crucial for achieving optimal performance.
mABC offers a comprehensive automated root cause analysis and resolution in
micro-services architecture and achieves a significant improvement in the AIOps
domain compared to existing baselines
