New benchmark tests whether AI Kubernetes tools know when to do nothing

A developer has released an open-source benchmark called a calibration and abstention harness, designed to evaluate AI-assisted Kubernetes SRE tools like K8sGPT not just on diagnosis accuracy but on their ability to withhold action under uncertainty. The benchmark covers 163 labeled incident cases spanning routine failures, ambiguous symptoms, cross-layer outages, and adversarial scenarios with misleading or stale evidence. The motivation stems from a real operational risk: a confident but incorrect automated remediation can escalate a manageable incident into a larger production outage. Each test case is scored across multiple dimensions, including root-cause identification, action safety, confidence calibration, and appropriate abstention. The project, available on GitHub, aims to raise the standard for how AI DevOps tools are evaluated in production-grade Kubernetes environments.