Last year, a deployment went sideways. An AI agent was running a data enrichment pipeline: pull records from an API, map fields into a schema, write to a database. Every API call returned 200 OK. The agent's dashboard showed green across the board. The agent reported success on every step. Six hours later, a downstream team flagged the data. Half the field mappings were hallucinated. The agent had confidently mapped company_revenue to employee_count , invented values for missing fields, and written duplicates for records it had already processed. Hundreds of bad rows, all marked as verified. Nobody noticed because nothing "failed." This is the fundamental problem with AI agents in production: the most dangerous failures look exactly like success. Traditional error handling — try/catch blocks, HTTP status code checks, crash monitoring — was built for deterministic software. AI agents are probabilistic systems. They don't crash when they're wrong; they confidently produce garbage with a 200 status code.…