arXiv cs.AI· Shihang Li, Thomas Anderson, Ratul Mahajan, Simon Peter, Luke Zettlemoyer·· 3 天前AI 评分42
自学习运维智能体 SDO：用 playbook 与检测器让分布式应用故障处理随时间改进
A Self-Defining Operator for Distributed Applications
AI 导读
研究者提出自学习运维智能体 SDO，通过将已解决故障的经验蒸馏为可复用的 playbook 和检测器脚本，让 LLM 智能体随处理事件增多而持续改进，而非每次从零排查。SDO 持续运行检测器，仅在其触发时调用 LLM，因此 token 成本随事件数量而非运维时长增长。在 11 个开源微服务应用上，SDO 将复发性故障的解决时间缩短 52%，并保持较低的单次事件成本，代码已开源。
正文 · 原文
Abstract:To operate distributed applications with high availability, operators must rapidly diagnose and mitigate any incident. LLM-based agents can help, but unlike experienced human operators they do not improve as they handle more incidents. Each investigation starts from scratch, re-deriving likely causes and re-testing hypotheses even when a similar fault was resolved in the past. We present a self-defining operator (SDO) that improves with time by updating its operational behavior and memory. When SDO resolves an incident, it distills that experience into playbooks, which describe reusable diagnostic instructions for future incidents with similar faults, and detectors, scripts that take application telemetry as input and output relevant playbook(s). SDO continuously runs the detectors and invokes LLMs only when one fires, so its token cost scales with incident count rather than with operational time. Across 11 open-source microservice applications, SDO cuts resolution time by 52% on recurring faults while keeping per-incident cost low. SDO is open source at this https URL.
| Subjects: | Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.14547 [cs.DC] |
| (or arXiv:2610.14547v2 [cs.DC] for this version) | |
| https://doi.org/10.48550/arXiv.2610.14547 arXiv-issued DOI via DataCite |
Submission history
From: Shihang Li [view email]
[v1]
Sat, 10 Oct 2026 04:08:13 UTC (2,590 KB)
[v2]
Wed, 14 Oct 2026 06:00:51 UTC (2,590 KB)
来源：arXiv cs.AI · arxiv.org