AI agent adoption is outpacing organizational governance capability, with formal AI policies declining even as deployment risk grows. No platform-level tooling exists to enforce policy, detect violations, or audit agent behavior across a fleet at runtime. The result is a widening gap where production agents operate outside any governance envelope.

Organizations deploying dozens or hundreds of AI agents have zero centralized way to enforce policies, detect violations, or audit agent behavior at runtime — creating existential compliance and reputational risk.

Platform engineering leads and CISOs at mid-to-large enterprises (500+ employees) running multiple AI agents in production across departments.

Enterprises already pay $50K-500K/yr for API gateways, SIEM tools, and compliance platforms — agent governance is the obvious next budget line as deployments scale, and regulatory pressure (EU AI Act, SOC2 AI controls) is creating a forcing function right now.

MVP is a lightweight proxy/SDK that sits between agents and their tool calls, enforcing declarative policy-as-code rules (rate limits, data access boundaries, action allowlists) with a dashboard showing real-time violations and audit trails — ship in 6-8 weeks using existing policy engine patterns (OPA/Cedar) adapted for agent action schemas.

Agent observability and governance is a greenfield slice of the $15B+ cloud security/compliance market; even 1% penetration in year one implies $150M+ TAM as every enterprise with agents in production becomes a buyer.