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Jev-Driven SRE Diagnosis: What Worked and What Failed
The SREGym team builds a Kubernetes incident-diagnosis pipeline with no LLM agent: a programmatic collector gathers cluster evidence and TypeSafe AI's Jev decision model picks root-cause candidates and supporting evidence. It passed 80 of 105 diagnoses (76.2%) across 21 SREGym-Lite faults with a 14.6s median, close to an LLM agent's score at far lower cost, and the post dissects the faults it failed.
7 min · 1,572 words