The Gap Between a Demo and a Deployed AI Agent There is a particular kind of optimism that happens in AI demos. The model responds intelligently. The tool calls execute cleanly. The output looks exactly right. Everyone in the room is excited. Then you put it in front of real users. Within 48 hours, you have edge cases the demo never surfaced. Inputs the model handles badly. Tool calls that fail in ways that aren't graceful. Latency that felt acceptable in a controlled environment but is unacceptable in production. A cost model that made sense for demo volume but looks alarming at real usage. I've been building production AI systems for the past three years — LLM-powered applications, autonomous agents, RAG pipelines, workflow automation. The gap between "impressive demo" and "reliable production system" is wider than most teams expect, and the failure modes are consistent enough that I can document them. This is that documentation. What Actually Fails in Production AI Agents 1.…