Unresolved production cloud incidents cost an average of over $2M per hour. This paper introduces PRAXIS, an orchestrator that manages and deploys an agentic workflow for diagnosing code- and configuration-caused cloud incidents. PRAXIS employs an LLM-driven structured traversal over two types of graph: (1) a service dependency graph (SDG) that captures microservice-level dependencies; and (2) a hammock-block program dependence graph (PDG) that captures code-level dependencies for each microservice. Compared to state-of-the-art ReAct baselines, PRAXIS improves RCA accuracy by up to 6.3x while reducing token consumption by 5.3x. PRAXIS is demonstrated on a set of 30 comprehensive real-world incidents that is being compiled into an RCA benchmark.
@article{arxiv.2512.22113,
title = {PRAXIS: Integrating Program Analysis with Observability for Root-Cause Analysis},
author = {Shengkun Cui and Rahul Krishna and Saurabh Jha and Ravishankar K. Iyer},
journal= {arXiv preprint arXiv:2512.22113},
year = {2026}
}
Comments
15 pages. Accepted to appear in The 56th Annual IEEE/IFIP International Conference on Dependable Systems and Networks