GALA: Graph-Augmented LLM Agents for Root Cause Analysis and Incident Response in Microservices
2026-08-10 • Software Engineering
Software EngineeringArtificial Intelligence
AI summaryⓘ
The authors developed GALA+, a method to find the root causes of problems in microservice systems by using graphs of service connections to guide its investigation. Unlike earlier methods that rely on only one type of data or can get confused, GALA+ uses multiple signals and limits its search through known relationships. It also provides ranked possible causes, summaries of incidents, and clear suggestions for actions. They created a new way to evaluate these results with human experts, showing that GALA+ performs better than previous approaches on test systems.
MicroserviceRoot Cause AnalysisTelemetryLarge Language ModelsService Dependency GraphTrace ScoringIncident ResponseSTRIXSURE-ScoreSite Reliability Engineering
Authors
Yifang Tian, Yaming Liu, Zichun Chong, Zihang Huang, Yiran Li, Hans-Arno Jacobsen
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.