Enterprise AI agents operating at scale inherit overly broad permissions from human users and service roles, with no lifecycle controls, runtime authorization tracking, or visibility to security teams. Traditional IAM systems were designed for human identities that authenticate once and behave predictably — they cannot govern agents that change behavior at runtime, call tools dynamically, and collaborate with other agents. This gap is blocking 80%+ of enterprises from moving agents to production and creating a 'shadow AI workforce' that security teams cannot see or audit.

Enterprise AI agents inherit overly broad human permissions with zero runtime governance, blocking production deployment and creating invisible 'shadow agent workforces' that security teams cannot audit or control.

CISOs and platform engineering leads at enterprises (1000+ employees) deploying or piloting AI agents across internal workflows, who are blocked by security review from moving agents to production.

Enterprises already spend $15-30B/yr on IAM (Okta, CyberArk, etc.) and their security teams are actively blocking agent deployments due to this exact gap — there is urgent budget and executive pressure to unblock AI initiatives, making this a purchase-order-ready problem today.

MVP is a lightweight policy-as-code sidecar/proxy that intercepts agent tool calls, enforces scoped permission policies per agent identity (not inherited human identity), and streams an audit log to a dashboard; integrate with one LLM framework (LangChain or CrewAI) and one cloud IAM provider (AWS IAM / Entra ID) first.

The IAM market is ~$20B and growing 12% YoY; agent-specific IAM could capture 10-20% of this as agents become a majority of non-human identities, yielding a $2-4B addressable segment within 3 years.