Agent oversight today relies on model-based verification — agents watching agents — which inherits correlated blind spots and cannot provide audit-grade assurance. The missing primitive is structurally independent verification: typed contracts, allowlists, and process boundaries whose source of truth exists outside any agent's control, analogous to financial audit separation of concerns. Without this, the reported 80% permission-violation rate in enterprise agent deployments cannot be meaningfully addressed.

Agent-watches-agent verification shares the same model blind spots, making enterprise trust impossible; teams need a structurally independent layer that enforces typed contracts, allowlists, and action boundaries from outside the agent stack.

Platform engineers and compliance leads at companies deploying autonomous agents in regulated or high-stakes workflows (fintech, healthcare, enterprise SaaS).

Enterprises are blocking agent deployments specifically because they can't demonstrate audit-grade control to security and compliance teams; a deterministic, model-free verification layer unblocks purchasing decisions worth millions in agent-platform spend.

MVP is a lightweight sidecar/proxy that intercepts agent actions against a declarative policy DSL (typed contracts, resource allowlists, rate limits) enforced via deterministic code — no LLM in the loop; ships as an SDK + dashboard with pre-built connectors for LangGraph, CrewAI, and OpenAI Assistants API.

Enterprise AI governance is projected at $4B+ by 2027; agent-specific policy enforcement is an emerging wedge within that, targeting the ~50K companies actively deploying or piloting autonomous agents.