Enterprise environments deploy AI agents using identity and access management systems designed for static human or VM-based identities, but agents make autonomous runtime decisions that change their effective permission requirements mid-execution. Existing IAM frameworks cannot model, scope, or audit the behavioral identity of an agent—only its credential set—leaving organizations with either over-provisioned agents or broken workflows. No agent-native access control layer exists that can dynamically adapt permissions to agent decision-making context while maintaining auditability.

Enterprises either over-provision AI agents (creating security risk) or break workflows with rigid permissions, because IAM was built for static human/VM identities, not autonomous decision-makers whose permission needs shift mid-execution.

Platform engineering and security teams at enterprises deploying autonomous AI agents across internal systems (finance, DevOps, customer ops).

Every enterprise deploying agents today faces a compliance/security blocker—CISOs won't approve production agent deployments without auditable access control, and the current workaround (service accounts with broad permissions) fails SOC2/SOX audits. Adjacent IAM spend (Okta, CyberArk) proves $10B+ willingness to pay for identity infrastructure.

MVP is a lightweight sidecar/proxy that intercepts agent API calls, evaluates them against a policy engine fed by the agent's current execution context (task graph, tool calls, user delegation chain), and grants/denies scoped ephemeral tokens—ship with OpenAI function-calling and LangChain integrations first, plus an audit log dashboard.

IAM is a $20B+ market; agent-native IAM captures a new wedge as every enterprise AI deployment (projected 70%+ of Fortune 500 by 2026) needs runtime access control purpose-built for agentic workloads.