GALA: Graph-Augmented LLM Agents for Root Cause Analysis and Incident Response in Microservices
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...
| Authors | Yifang Tian, Yaming Liu, Zichun Chong et al. |
|---|---|
| Primary category | cs.SE |
| Published | 10 August 2026 |
| Last updated at source | 10 August 2026 |
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