Gartner thinks 40% of agentic AI projects get canceled by 2027. Building one right now, I believe it.

I saw that stat again this week and it landed differently than it would have a year ago. Not because the models got worse, they clearly didn't, but because the actual failure mode nobody warns you about is the demo working great and then the illusion cracking quietly in production. Not a crash, not an error, just the agent confidently continuing on bad data and nobody noticing until three days later when the numbers look wrong. It feels like most teams are still measuring the wrong thing, can it complete the task once, instead of what happens the hundredth time when a tool response is malformed or a downstream API times out. That gap is where I'd bet most of that 40% cancellation number comes from, not the model being incapable, just nobody building for the version of the system that fails quietly. Curious if others building in production are seeing the same thing, or if this stat feels overblown from where you're sitting.