Enterprises can test AI agents but cannot deploy them at scale because no architectural layer exists to verify that an agent's runtime actions match organizational intent — IAM and prompt engineering are insufficient once agents interpret untrusted inputs and take real-world actions. The 67-point gap between enterprise testing (72%) and production deployment (5%) reflects a structural trust deficit, not a technical one. A coordination layer that provides verifiable execution governance and action auditing is the missing precondition for enterprise adoption.

Enterprises cannot move AI agents from testing to production because no runtime layer exists to verify agent actions match organizational policies — IAM controls identity, not intent, and prompt engineering crumbles against untrusted inputs.

Enterprise platform engineering and CISO teams at companies with 500+ employees who have built agent prototypes but are blocked from production deployment by compliance, legal, or risk teams.

The 72% testing vs 5% deployment gap represents billions in stranded AI investment; enterprises already pay $50-500K/yr for API gateways, SIEM, and policy engines — this is the missing equivalent for agentic systems, and procurement urgency is driven by board-level AI deployment mandates hitting immovable compliance walls.

MVP is a sidecar proxy that intercepts agent tool calls (API requests, DB writes, code execution) against a policy DSL defining permitted action envelopes per agent role — ship as an OPA-style policy engine with an immutable audit log, integrating with LangChain/CrewAI/AutoGen in week one.

Enterprise AI governance is a $4-8B emerging market nested within the $28B GRC market; every enterprise deploying agents (projected 60%+ of Fortune 500 by 2026) needs this layer.