GALA: Graph-Augmented LLM Agents for Root Cause Analysis and Incident Response in Microservices

• URL: http://arxiv.org/abs/2608.08968v1
• Date: Mon, 10 Aug 2026 00:15:07 GMT
• Title: GALA: Graph-Augmented LLM Agents for Root Cause Analysis and Incident Response in Microservices
• Abstract summary: We present GALA+, a graph-augmented LLM agentic framework centered on graph-guided investigation.<n>GALA+ produces ranked diagnoses, incident summaries, and stratified action recommendations.<n>On two microservice benchmarks, GALA+ consistently achieves the strongest overall results.
• Score: 7.985937450027174
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Microservice root cause analysis (RCA) requires correlating failures across heterogeneous telemetry within complex service dependency graphs. Existing methods often rely on a single telemetry modality; recent LLM-based approaches can suffer from unconstrained exploration and hallucination; and most systems stop at fault ranking without producing actionable incident response. We present GALA+, a graph-augmented LLM agentic framework centered on graph-guided investigation, which uses service dependencies to bound exploration and refine diagnosis through localized multi-modal evidence. For initial hypothesis generation, GALA+ combines complementary telemetry signals with STRIX, a novel trace- and graph-structure-aware scoring module. GALA+ then produces ranked diagnoses, incident summaries, and stratified action recommendations. We further introduce SURE-Score, a human-guided evaluation framework co-developed with industry SRE experts for assessing RCA-specific output quality beyond conventional text similarity metrics. On two microservice benchmarks, GALA+ consistently achieves the strongest overall results, surpassing the best LLM-based baseline by more than 25 percentage points in AC@1, while also receiving the highest ratings from both SURE-Score and independent human SRE evaluation.

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