Auditable Graph-Guided Root Cause Analysis for Kubernetes Incidents

Researchers present Graph Traversal Agent, an LLM-based root cause analysis system for Kubernetes incidents that combines graph-guided reasoning with deterministic validation tools. The system demonstrates significant performance improvements on benchmarks but acknowledges limitations in production environments and benchmark-specific coupling.

The paper addresses a critical pain point in cloud infrastructure management: reliably diagnosing Kubernetes incidents through auditable, evidence-based reasoning rather than heuristic shortcuts. Graph Traversal Agent integrates large language models with structured graph traversal and validation mechanisms, enabling systems to reason over typed evidence while maintaining operational constraints like read-only access and bounded execution. This hybrid approach represents a meaningful evolution in incident response automation, where AI reasoning power is constrained by deterministic verification rather than operating unchecked.

The research emerges from growing recognition that LLM-based troubleshooting requires rigorous validation frameworks to move beyond scenario-specific pattern matching. Previous incident analysis systems often achieved high scores through prompt engineering tricks rather than generalizable understanding. This work's ablation studies and lightweight validation checks—same-judge comparison, cascade-source verification, and telemetry leak testing—establish methodological rigor often absent in AI systems papers.

However, the authors demonstrate unusual honesty about limitations. While achieving 0.91 F1-score on benchmarks, performance degrades significantly when scenario-specific prompts are removed (0.70 F1), and gains concentrate on ChaosMesh scenarios where root causes are pre-injected into evidence graphs. Live-cluster trials proved insufficiently stable for production validation. These candid limitations matter more than headline numbers for practitioners evaluating adoption.

The work signals growing maturity in AI-assisted infrastructure management, where teams increasingly demand explainable, auditable systems over black-box solutions. Infrastructure teams and cloud platforms will likely adopt similar graph-plus-validation approaches as incident complexity scales, though production deployment remains challenging.

• →Graph Traversal Agent combines LLM reasoning with deterministic graph operations and separate validation stages for Kubernetes incident diagnosis.
• →Rigorous ablation studies reveal performance drops from 0.91 to 0.70 F1 when scenario-specific prompting is removed, indicating benchmark coupling.
• →Lightweight validation mechanisms including prompt ablation and telemetry leak tests establish reproducible evaluation standards often missing in AI systems.
• →Live-cluster instability prevented production-readiness claims, reflecting real-world challenges in deploying AI systems to critical infrastructure.
• →The hybrid approach of constraining LLM reasoning with deterministic tools represents a practical template for auditable AI in DevOps contexts.