Eval harness catches confident AI errors

An enterprise root-cause explainer looked reliable in qualitative review but an evaluation harness found its highest confidence on the cases where it was most wrong, after testing against 3 classes of synthetic ground-truth scenarios: schema changes, transformation bugs, and overlapping signals. The account of the harness says overlapping signals were hardest, while scoring both whether the right cause appeared and how it ranked across the test set. Teams shipping AI into triage or compliance workflows should log calibrated accuracy against known cases, not accept “sounds reasonable” as a test; the site’s OfficeQA analysis makes the same distinction between fluent output and grounded work.