The Hallucination Nobody Caught: Why AI Agents Need Boundary Checks, Not Better Prompts

Building AI agents is easy when inputs match your assumptions—production is where reality differs. This story reveals how LLMs confidently fabricate plausible data when facing unexpected nulls, and why the best fix isn't smarter models but deterministic validation before the model touches input. It's essential knowledge for anyone deploying agents: reliability comes from boundary checks, not algorithmic sophistication.