Traditional identity and access management systems assume static, human-operated principals and are fundamentally inadequate for agents that authenticate continuously, modify behavior at runtime, and delegate permissions to sub-agents. With 97% of organizations reporting AI security incidents lacking AI-dedicated access controls, and MCP adoption outpacing MCP security, the gap between agent capability and access governance is widening rapidly. No integrated, agent-native IAM layer exists that handles dynamic permission scoping, delegation chains, and least-privilege enforcement across the agent lifecycle.

Traditional IAM assumes static human principals and cannot handle agents that spawn sub-agents, escalate permissions at runtime, and authenticate continuously — leaving 97% of orgs with AI security gaps.

Platform engineering and security teams at mid-to-large enterprises deploying autonomous AI agents across internal tools and customer-facing workflows.

Enterprises already pay $50K-500K/yr for IAM solutions (Okta, CyberArk) and are desperate to extend governance to agents before regulators force it; the MCP adoption wave means the pain is acute NOW and no incumbent covers dynamic agent delegation chains.

MVP is an open-source policy engine (OPA-based) with an agent-native SDK that intercepts MCP tool calls, enforces scoped permissions per agent/sub-agent with delegation depth limits, and logs full chain-of-custody — ship as a sidecar or middleware in 8-12 weeks.

IAM market is $20B+ and growing 13% CAGR; agent-specific IAM is a new wedge that could capture 5-10% as agent adoption scales, yielding a $1-2B segment within 3 years.