"Fail closed, not silent: what an AI system should do when it isn't sure it worked"
An AI system that fails loudly is an inconvenience. One that returns a confident wrong answer is a liability. Designing explicit failure states — and making the system stop when it isn't sure — is what separates the two.
A quiet wrong answer is the most expensive thing an AI system can produce. A loud failure gets noticed: someone retries, files a ticket, fixes the cause. A quiet wrong answer sails downstream wearing the same formatting as a correct one — into an invoice, a CRM record, a customer reply — and by the time anyone notices, it has been acted on. The cost is not the error. The cost is everything built on top of it.
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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