Why AI Agents Fail in Production—And How to Catch It Before Deployment

AI agents typically fail not because models are inadequate, but because organizations lack clear success metrics and systematic evaluation processes. The article provides a practical framework—baseline testing, regression validation, tool-calling verification, and release gates—that enables teams to identify failures before users encounter them in production, preventing costly real-world breakdowns.