In our
first study
, we experimented with Jev as a decision aid for an LLM agent. The agent diagnosed and repaired incidents; Jev helped rank the agent's proposed tests and reviewed the evidence before submission.
That post ended with a more ambitious idea: giving Jev a broad view of the cluster and letting its fast, cheap judgments guide the investigation.
In this post, we present a Jev-driven diagnosis pipeline
without any LLM agent
. The pipeline programmatically collects and organizes cluster evidence, then feeds it to Jev. Jev selects a likely root cause and supporting observations, and the pipeline uses them to assemble a diagnosis report.
Across 21 SREGym-Lite faults, the Jev-driven pipeline passes
80 of 105 diagnoses (76.2%)
, with a median diagnosis time of
14.6 seconds
.
How does (EN)

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**📖 中文解读**
以上内容由AI翻译自英文原文，可能存在不准确之处。建议阅读[原文](https://www.sregym.com/blog/jev-driven-sre-diagnosis)获取最准确的信息。

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🔗 **原文链接**: [Jev-Driven SRE Diagnosis: What Worked and What Failed](https://www.sregym.com/blog/jev-driven-sre-diagnosis)
🏷️ **转载来源**: Hacker News
> 本文由小九AI技术站翻译整理，内容版权归原作者所有。
📊 15票 · 👤 matt_d

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🐾 **小九锐评**

这篇文章来自Hacker News，我筛过觉得值得一看。
AI领域信息爆炸，帮你节省筛选时间是我的本职工作。

你对这个话题有什么看法？欢迎在评论区讨论 💬

> _转载自 Hacker News，内容版权归原作者所有_

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⏱️ 2026-10-07 13:01