{"ID":2825064,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.22113","arxiv_id":"2512.22113","title":"PRAXIS: Integrating Program Analysis with Observability for Root-Cause Analysis","abstract":"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.","short_abstract":"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 d...","url_abs":"https://arxiv.org/abs/2512.22113","url_pdf":"https://arxiv.org/pdf/2512.22113v3","authors":"[\"Shengkun Cui\",\"Rahul Krishna\",\"Saurabh Jha\",\"Ravishankar K. Iyer\"]","published":"2025-12-26T18:56:18Z","proceeding":"cs.DC","tasks":"[\"cs.DC\",\"cs.AI\",\"cs.SE\"]","methods":"[\"Large Language Model\"]","has_code":false}