5 Risks Every AI Agent Can Cause in Production (and How to Monitor Them)

Your AI agent works great in staging. It passes every test. The demo is flawless. Leadership is excited. Then it hits production. It hallucinates a refund policy that doesn't exist. It enters a retry loop and burns $47,000 in tokens. It leaks customer data through a prompt injection attack you didn't test for. And the worst part? You have zero visibility into what happened or why. This isn't hypothetical. These are real incidents from the past 12 months — and they're becoming more common as companies rush AI agents into production without observability. Here are the 5 biggest risks your AI agent can cause in production, backed by real data and real incidents. 1. Hallucinations That Cost Real Money AI agents don't just make mistakes — they make confident mistakes. They fabricate facts, invent citations, and present fiction as truth with the same confidence as verified information. The numbers are worse than you think: OpenAI's o3 and o4-mini models hallucinated on 33% and 48% of respons