Your agent returns a confident answer. Half are fabricated, and your test suite says PASS. Research shows standard metrics miss 65 to 93% of safety violations (AgentDrift, March 2026): agents invent amenities never in the search results and drift from safe to harmful advice across turns. Binary pass/fail sees "task completed" and misses the lie. Zero-shot hallucination detection finds fabricated facts with no training data. Linear Semantic Consistency (Oct 2025) hits 84.6% AUROC by probing the model's internal states, training free across model families. Claim decomposition verifies atomic statements at 88.4% precision. You'll learn when to use each versus a real-time LLM judge. Trajectory monitoring catches behavioral drift, where an agent slides from legal strategy to gray-area optimization to tax evasion across turns. You'll add per-turn safety scoring that flags drops over 0.3, plus real-time guardrails using lifecycle hooks that swap unsafe output for a safe fallback at 120ms. 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