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Why Agent Evaluation Needs Confidence
Once agents begin doing real work, evaluation has to answer reliability, coverage, and stability instead of "it looks impressive."
The most common claim in agent demos is still: “it already looks usable.”
That is not enough once an agent moves into real tasks. Users need to know the probability of success, whether that probability stays stable across tasks, whether the judge is trustworthy, and whether the test set overfits the system’s strengths.
This is why I care about confidence intervals, judge agreement, coverage that resembles real task distributions, and repeated-run stability.
If a benchmark cannot help us decide whether an agent deserves a place in a real workflow, it is just another interface illusion.
Configure the public Giscus environment variables to open discussion on the public site.