Auditable Graph-Guided Root Cause Analysis for Kubernetes Incidents

Abstract:Kubernetes incidents are diagnosed reliably only when a root-cause system's reported gains come from incident evidence rather than scenario-specific shortcuts. We present Graph Traversal Agent, a graph-guided RCA agent that combines LLM reasoning with specialized tools. The model reasons over a typed evidence graph, while deterministic graph and tool operations collect evidence, bound the search, and check proposed verdicts. We map operational constraints, including read-only evidence collection, propagation-aware diagnosis, bounded execution, and independently validated verdicts, to a typed incident graph, a LangGraph traversal state machine, and a separate validation stage. On ITBench snapshots scored by one fixed qwen-plus judge, the audited system raises root-cause-entity F1 over an earlier iteration of the same system from 0.6087 to 0.9130 on a 23-scenario common subset. A prompt-level ablation separates prompt-tuned gains from gains that survive once scenario-specific hints are removed: the stripped-prompt configuration retains 0.6958 F1 on a 19-scenario subset. The surviving gain concentrates on ChaosMesh scenarios whose ground-truth root cause is the injected fault object already present in the evidence graph, so we report it as benchmark-coupled rather than broad cross-cluster RCA evidence. Lightweight checks, including same-judge comparison, prompt-level ablation, cascade-source checking, and a telemetry no-leak test, mark claims as supported, pending, or out of scope. We scope the work to ITBench OpenTelemetry-demo snapshots. Live-cluster trials served as an engineering stress test, but alert state and trace availability did not stay stable enough for controlled scoring, so we make no production-readiness or mean-time-to-repair claim.

8 pages, 1 figure. Preprint

Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)

arXiv:2606.08590 [cs.SE]

(or arXiv:2606.08590v1 [cs.SE] for this version)

https://doi.org/10.48550/arXiv.2606.08590 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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