Jev-Driven SRE Diagnosis: What Worked and What Failed
Researchers developed a Jev-driven diagnosis pipeline that uses programmatic evidence collection and focused model decisions to identify root causes in Kubernetes clusters without an LLM agent. In testing across 21 SREGym-Lite faults, the pipeline successfully passed 76.2% of diagnoses with a median response time of 14.6 seconds. The system works by having Jev select from predefined options to classify components and evidence, allowing the pipeline to assemble accurate diagnosis reports.
- ▪The Jev-driven pipeline achieved a 76.2% success rate on 21 SREGym-Lite faults with a median diagnosis time of 14.6 seconds.
- ▪The system uses a programmatic collector to gather Kubernetes objects, events, and logs, which are then summarized for Jev to process.
- ▪Jev functions as a decision aid by selecting from supplied options rather than generating commands or writing the final report.
- ▪In a specific test case, the pipeline correctly identified a mutating admission webhook as the root cause of a memory limit mismatch in the nginx-thrift deployment.
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| Original publisher | SREGym |
| Canonical URL | https://www.sregym.com/blog/jev-driven-sre-diagnosis |
| Publication time | Wed, 07 Oct 2026 01:27:49 +0000 |
| Retrieval time | 2026-10-07T03:36:36.028Z |
| Last seen | 2026-10-07T03:36:36.028Z |
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| Summary source text | contentText |
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| Cluster | z5zYOYbtwJ6D · 1 stories |
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| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
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| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
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Opening excerpt (first ~120 words) tap to expand
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.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at SREGym.
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