Most teams review an AI feature's failures closely and its successes not at all. That split makes sense on the surface — failures are where the damage is — but it quietly assumes that a successful run succeeded for the right reasons. It often didn't.
A model can produce a correct-looking answer while reasoning from a coincidence rather than the actual signal you intended it to use: a keyword that happened to correlate with the right output in your training and test data, a retrieved document that was irrelevant but harmless this time, a tool call that worked because the input happened to be well-formed. None of that shows up as a failure. It shows up as a pass, right up until the input shifts slightly and the same shortcut produces a confidently wrong answer instead.
This is why a periodic look at *successful* runs earns its keep, not just an inbox of failure reports. Pull a small, genuinely random sample — not the ones flagged by users, which are already failures by definition — and ask not "was the output correct" but "was the output correct for the reason I'd want." Check what the model actually attended to, which tool calls it made and in what order, whether a RAG answer used the document that was actually relevant or a different one that happened to contain similar words.
The payoff isn't glamorous. You mostly won't find anything. But the rare case where you do — a shortcut masquerading as competence — is exactly the failure mode that no amount of failure-log review would have caught, because it hadn't failed yet.