The AI Feature That Works in the Demo and Breaks in Production

Last Updated on August 24, 2026 by Editorial Team Author(s): EMMANUEL NWANGUMA Originally published on Towards AI. Vendors sell a fix for it. I ran 833 tests across 6 AI models to see whether the fix actually works. Mostly, it didn’t. Imagine you hire a very fast, very well-read assistant to process your customer support inbox. The article explains why small AI errors can break real systems—especially when models are chained—showing that even a “strict enforcement” fix often doesn’t help and can even worsen results. Through extensive testing of multiple models across different constraint settings, the author finds that the true drivers of failure are model capability and form complexity, while common monitoring assumptions (like checking for “well-formed” output) miss the most damaging issues. The author also highlights measurement pitfalls, including how test design can force misleading failures and how evaluators can agree with each other while still being wrong. Finally, the piece offers practical guidance for builders and evaluators (pick models before tuning infrastructure, avoid illegal forced answers, validate what providers actually do, and account for compounded step-by-s…