Traditional security models built on request-response validation are insufficient for agentic AI systems that construct workflows dynamically at runtime, creating emergent behaviors accumulating risk across sequences rather than isolated requests. The proposed solution shifts from request validation to continuous behavioral governance through constraint-based policies defining operating boundaries such as resource limits, quantitative caps, capability escalation, and conditional approval gates.