Hi! Wanted to share something that’s been on my mind after a lot of conversations with teams trying to ship AI agents. Over 80% of AI projects fail in production. That stat gets thrown around a lot. What doesn’t get discussed is why. The easy answer is “the model wasn’t good enough” or “the data was bad.” And sometimes that’s true. But what I keep seeing is something different: teams launch without ever having defined what “working well” actually means. They test a few prompts. The demo looks good. Someone in a meeting says “it seems solid.” And then it goes live. The problem is that an AI agent isn’t a static system. A chatbot responds. An agent interprets, decides, and acts. And the more it can do, the more surface area there is for things to go wrong, not in obvious ways but in subtle ones. It doesn’t hallucinate on the demo. It hallucinates on the edge case nobody thought to test. It escalates correctly 95% of the time. That other 5% is a customer getting a wrong answer with full confidence. What I think is actually missing in most rollouts is a definition of failure before launch. Not just “does it answer correctly” but: does it know when not to answer? Does it escalate when it should? Does it stay within its boundaries when the conversation goes somewhere unexpected? A good average score doesn’t cancel out a critical error. That’s the part that gets skipped. Is anyone else seeing this gap between how AI agents perform in controlled testing vs. what actually happens when real users start pushing on them? submitted by /u/hubtyper
Originally posted by u/hubtyper on r/ArtificialInteligence