用AI自动处理电商系统警报，提速90%洞察速度。
Agentic Observability: Automated Alert Triage for Adobe E-Commerce
- AI代理基于ReAct框架自动分析日志、定位故障服务。
- 生产环境实测平均洞察时间缩短90%，诊断准确率相当。
- 适合需要快速响应的大型企业运维团队参考。
现代企业系统因复杂依赖关系，使可观测性与事件响应愈发困难。人工警报排查（包括日志检查、API验证和知识库交叉比对）仍是降低平均恢复时间（MTTR）的主要瓶颈。本文提出一种部署于Adobe电商基础设施的智能可观测性框架，采用ReAct范式实现警报自动排错。警报触发后，该代理能动态识别受影响服务，在分布式系统中检索并分析关联日志，并规划上下文相关的操作，如查阅手册、执行运行手册或检索增强型代码分析。生产部署结果表明，相比人工排查，平均洞察时间减少90%，诊断准确率保持相当。结果证明，智能体AI可实现排错延迟量级下降和诊断准确率跃升，标志着企业运维向自主可观测性的关键转变。
原文摘要 · Abstract (English)
Modern enterprise systems exhibit complex interdependencies that make observability and incident response increasingly challenging. Manual alert triage, which typically involves log inspection, API verification, and cross-referencing operational knowledge bases, remains a major bottleneck in reducing mean recovery time (MTTR). This paper presents an agentic observability framework deployed within Adobe's e-commerce infrastructure that autonomously performs alert triage using a ReAct paradigm. Upon alert detection, the agent dynamically identifies the affected service, retrieves and analyzes correlated logs across distributed systems, and plans context-dependent actions such as handbook consultation, runbook execution, or retrieval-augmented analysis of recently deployed code. Empirical results from production deployment indicate a 90% reduction in mean time to insight compared to manual triage, while maintaining comparable diagnostic accuracy. Our results show that agentic AI enables an order-of-magnitude reduction in triage latency and a step-change in resolution accuracy, marking a pivotal shift toward autonomous observability in enterprise operations.
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