一句话：提出图增强 LLM 智能体框架 GALA+，用服务依赖约束探索，实现微服务根因分析、事件总结与分层响应建议。
微服务根因分析需在复杂服务依赖图中关联异构遥测，现有方法常依赖单一模态，LLM 方法易无约束探索和幻觉，且多止于故障排序而缺少可执行响应。GALA+采用图增强 LLM 智能体框架，以服务依赖限定探索范围，通过局部多模态证据细化诊断；其 STRIX 模块结合 trace 与图结构感知评分生成初始假设，再输出排序诊断、事件摘要和分层行动建议。作者还提出 SURE-Score 人工引导评估框架。在两个微服务基准上，GALA+整体最优，AC@1 超过最佳 LLM 基线 25 个百分点以上，并获得 SURE-Score 与独立 SRE 人工评估最高评分。
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.
@inproceedings{paperbot12235,
title = {GALA: Graph-Augmented LLM Agents for Root Cause Analysis and Incident Response in Microservices},
author = {Yifang Tian and Yaming Liu and Zichun Chong and Zihang Huang and Yiran Li and Hans-Arno Jacobsen},
booktitle = {ASE},
year = {2026},
doi = {10.1145/3832783.3834335}
}