Graph Traversal Agent improves root-cause F1 from 0.6087 to 0.9130 on ITBench snapshots but the gain is benchmark-coupled to cases where the injected fault is already in the evidence graph.
PRAXIS: Integrating Program Analysis with Observability for Root-Cause Analysis
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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.
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cs.SE 1years
2026 1verdicts
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Auditable Graph-Guided Root Cause Analysis for Kubernetes Incidents
Graph Traversal Agent improves root-cause F1 from 0.6087 to 0.9130 on ITBench snapshots but the gain is benchmark-coupled to cases where the injected fault is already in the evidence graph.