Users and platform operators lack visibility into what actions AI agents actually perform after being granted OAuth or delegated access scopes. Current authorization models record the grant but not the downstream execution, meaning there is no way to verify scope compliance, audit decisions, or revoke selectively mid-session. This creates systemic accountability gaps that existing OAuth infrastructure was never designed to close for agentic, multi-step workflows.

After granting an AI agent OAuth or delegated access, there's zero visibility into what it actually did—no scope compliance verification, no per-action audit log, and no way to selectively revoke mid-session.

Platform operators and enterprise security teams deploying AI agents with OAuth/API access to sensitive systems (CRMs, codebases, cloud infra).

Enterprises are already blocked from deploying agents in regulated environments (finance, healthcare, gov) because they can't prove compliance; this unlocks budgets already earmarked for agent adoption by closing the audit gap that existing IAM/OAuth tooling ignores.

Ship a lightweight SDK/proxy layer that intercepts agent API calls, logs each action against granted scopes in an append-only ledger, and exposes a dashboard with real-time alerts and per-action revocation—MVP targets OpenAI function-calling and LangChain tool-use patterns.

Subset of the $18B+ IAM market expanding into the new agentic AI layer; every company deploying agents with API access is a buyer.