"The confident wrong answer that cost trust, not money"
One wrong-but-plausible extraction reached a human who had learnt to rely on the system, and the damage was not measured in money. It was measured in weeks of double-checking that erased the automation's entire value. The fix was a confidence gate — and a change in how I think about what these systems actually spend.
You can put a number on most automation failures. A crashed workflow costs the minutes it takes to restart. A burst of retries costs a line on the API bill. The failure that cost me most had no number attached at all, because what it burned was not money. It was trust.
Have an AI feature stuck between demo and production?
The gap — reliability, evals, cost control, the plumbing that keeps it running unattended — is exactly the work I do. If that sounds familiar, a short conversation is usually enough to point you the right way.
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