Your agent gives a confident answer. It sounds right, your tests say PASS, and it is completely made up. A test that only checks "did it finish the task" never catches a lie, because the agent looks just as sure when it invents an amenity as when it reports a real one. It gets more dangerous across a conversation. An agent can start safe and slowly drift, one reasonable sounding turn at a time, into advice it should never give. If you only grade the final answer, you miss the moment it went wrong. This talk shows how to catch both: spot a fabricated answer without any training data, score every turn so you see drift as it happens, and add a guardrail that swaps an unsafe answer for a safe one before the user ever sees it. You'll walk away with: • Zero-shot detection that needs no labeled training data • Per-turn monitoring that catches drift before harm • Real-time guardrails that block unsafe output before delivery