Enterprise deployments of AI agents lack built-in consent checkpoints, authorization tiers, and accountability tracking matched to actual risk profiles. Only 14% of security leaders allow agents to act unsupervised, yet 57% of enterprises have zero formal governance controls, and 97% expect a major incident within the year. Existing frameworks provide no market-standard mechanism for detecting compromised credentials, attributing agent-caused incidents, or enforcing privilege boundaries at runtime.

Enterprises deploying AI agents have no standardized way to enforce authorization tiers, consent checkpoints, or accountability tracking at runtime — leaving 57% with zero formal controls while 97% expect a major incident within a year.

CISOs and platform engineering leads at enterprises (Series C+ or F500) deploying autonomous AI agents across internal workflows, customer-facing products, or DevOps pipelines.

Security and compliance teams are actively blocking agent deployments due to ungoverned risk; this unlocks stalled revenue-generating AI initiatives. Enterprises already pay $50K-500K/yr for API gateways, IAM, and compliance platforms — this is the missing agent-native equivalent at a moment when deployment pressure from leadership is intense.

MVP is a lightweight policy-as-code sidecar/proxy that intercepts agent tool calls, evaluates them against configurable risk-tiered policies (auto-approve, human-in-the-loop, deny), and logs an immutable audit trail — ship SDKs for LangChain, CrewAI, and OpenAI Assistants API first, with a dashboard for policy management and incident replay.

AI security and governance tooling is projected at $7B+ by 2027; the agent-specific governance slice targeting the ~200K enterprises deploying agents is a $2B+ near-term opportunity.