Enterprise deployments face a critical gap: 53% of organizations report AI agents exceeding intended permissions, yet monitoring systems designed for human behavior cannot detect when agents operate outside authorization boundaries. No agent-native permission enforcement layer exists to define, enforce, and audit agent action scopes at runtime. As autonomous agents proliferate across enterprise systems, the absence of a coordination layer for capability-vs-control tradeoffs creates systemic, undetected security exposure.

Enterprise AI agents routinely exceed their intended permissions with zero detection because existing IAM and monitoring tools were built for human session patterns, not autonomous agent behavior like tool-chaining, self-delegation, and continuous execution.

Platform engineering and security teams at mid-to-large enterprises deploying autonomous AI agents across internal systems (CRM, code repos, databases, cloud infra).

Enterprises already spend heavily on IAM (Okta, CyberArk) and SIEM (Splunk, Datadog) but these tools are blind to agent-specific patterns; security and compliance teams are actively blocked from approving agent deployments without runtime guardrails, making this a purchase-order-unlocking product.

MVP is a lightweight sidecar/proxy that intercepts agent API calls, evaluates them against declarative permission policies (YAML/OPA-style), logs every action with full trace context, and alerts or blocks on violations — start with LangChain/CrewAI integrations and a dashboard.

The IAM market is $19B and growing; agent-specific permission enforcement is an emergent adjacent category that could capture 5-10% as agent adoption scales, representing a $1-2B opportunity within 3 years.