The Real Cost of AI Agent Hallucination in Production

You deployed your AI agent. The API calls are cheap. Token costs are logged. You're watching costs in a spreadsheet. The number that doesn't show up in the spreadsheet: the downstream cost of a hallucinated output. An LLM generating wrong text in a chatbot is annoying. An LLM fabricating a payment amount in a dunning email, inventing a ticket status in a dev briefing, or confidently filling in company funding data it doesn't have, those are different problems. The damage isn't the API call. It's what happens after the output leaves your system. Here are three categories I've hit in production across real agents, with the patterns I now use to handle them. Category 1: Fabrication in Structured Output Structured output hallucination happens when a model fills in fields it has no data for. Instead of returning null, it invents something plausible. Scout is a sales research agent that takes a company name, scrapes five sources (website, Google News, LinkedIn, Crunchbase, job listings), the