用图结构引导大模型，精准定位微服务故障根源并给出可操作建议
GALA: Graph-Augmented LLM Agents for Root Cause Analysis and Incident Response in Microservices
- 基于服务依赖图约束大模型探索范围，结合多模态日志生成诊断假设
- 在两个基准上AC@1指标领先基线25%以上，且获得专家最高评分
- 适合运维工程师和SRE团队快速响应线上故障，提升故障处理效率
微服务根因分析（RCA）需要在复杂的服务依赖图中关联异构遥测数据。现有方法通常依赖单一遥测模态；近期基于大模型的方法存在探索无约束和幻觉问题；多数系统仅做到故障排序而无法生成可操作的应急响应。本文提出GALA+，一个基于图增强的大模型智能体框架，以服务依赖关系引导调查过程，通过局部多模态证据缩小探索范围并优化诊断。初始假设生成阶段，GALA+融合互补遥测信号与STRIX——一种新型的、对追踪和图结构敏感的评分模块。GALA+随后生成排名诊断结果、事件摘要及分层行动建议。我们还引入SURE-Score，一种由行业SRE专家共同开发的人工评估框架，用于衡量RCA输出质量，超越传统文本相似性指标。在两个微服务基准测试中，GALA+持续取得最优整体表现，在AC@1上比最佳大模型基线高出超过25个百分点，同时在SURE-Score和独立人工评估中均获得最高评分。
原文摘要 · Abstract (English)
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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