A seemingly perfect demo showcasing forty-three successful test cases of an agent processing structured input and writing results downstream concealed a critical flaw. Upon deployment, the agent produced database records with incorrect 1970 dates, stemming from an unforeseen input date format in production. The agent robustly yet incorrectly interpreted ambiguous dates, leading to subtle data corruption without errors or warnings. This wasn't a hallucination but a confident misinterpretation that went unnoticed because nothing visibly broke. The author implemented input and output validators to address this issue, ensuring data integrity before and after agent processing. These wrappers check field existence, data formats, and value ranges, routing any failures to a review queue. This prevents silent corruption without altering the agent's core model or logic. The fix, an hour of uninteresting code, highlights that reliability in production depends on robust engineering, not just agent capability. Production environments present unpredictable inputs, unlike controlled demo cases. The author now applies this validation pattern to all agents writing to persistent systems, defining clear boundaries for agent operation. Reliability, a feature demos rarely highlight, is paramount when an agent encounters unforeseen real-world scenarios.