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.
