Your AI agent just leaked an SSN, cost surged and your tests passed. Here's why.

Your agent tests pass. Your monitoring says "green." Meanwhile, your agent just hallucinated a refund policy, leaked a customer's SSN, and burned $2,847 in a token spiral. The Problem AI agents fail silently. Your HTTP monitoring sees 200s. Your latency metrics look normal. Your error rate is zero. But your agent is failing. Hard. What Monitoring Sees What Actually Happened HTTP 200, normal latency 500 → 4M tokens, $2,847 over 4 hours HTTP 200, fast response Confident, completely wrong answer Successful response Customer SSN in the output Tool call succeeded Called delete_order instead of lookup_order No change in metrics Model update degraded quality by 30% You can't curl your way out of this. You can't grep logs for hallucinations. You need agent-aware testing . What agenteval Does Write agent tests like regular Python tests. Run them in CI. Catch failures before production. def test_agent_no_hallucination ( agent , eval_model ): result = agent . run ( " What is our refund policy? "