AI-assisted DevOps and SRE tools are becoming more common. Tools like K8sGPT can scan Kubernetes clusters, detect issues, and explain what might be going wrong.
Most evaluations of these tools focus on one obvious question:
Can the system diagnose or fix the incident?
That question matters, but in production systems, it is not the whole story.
Sometimes the more important question is:
Does the system know when not to act?
A confident but wrong remediation can make an incident worse. Restarting the wrong workload, suggesting an unsafe mutation, acting on stale events, or proposing changes based on incomplete evidence can turn a manageable incident into a larger production problem.
That idea is the motivation behind this benchmark.
A calibration and abstention benchmark has been built for K8sGPT-backed Kubernetes SRE workflows. The goal is not only to test whether a workflow can identify Kubernetes issues, but also whether it can recognize uncertainty, avoid unsafe actions, and abstain when the evidence is incomplete, misleading, or adversarial.
GitHub repo:
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