Access control for multi-user AI agents

AIThis description was written by a model from public signals. The facts behind it are listed under Evidence.

Problem

Developers building AI agents that act on behalf of multiple users lack a clean way to enforce access control and audit who did what. Today they either share a single API key (losing user attribution) or build custom authentication logic themselves, which is complex, error-prone, and especially difficult in regulated industries.

Solution concept

A platform that acts as a proxy between agents and downstream APIs, managing credentials per user and enforcing fine-grained runtime policies tied to user identity. It automatically attributes actions to the person the agent acts on behalf of and maintains compliance-grade audit logs, eliminating the need for custom auth logic.

Who has it

Software developers and companies building internal AI agents in regulated industries (financial services, defense, healthcare) who need user-level access control and compliance audit trails.

How they handle it today

Currently, agents in Slack or other interfaces sign in with shared API keys to downstream systems like CRMs, billing platforms, or payroll software. This means the agent can perform any action the shared key permits, with no per-user access control. Developers either accept this limitation or build custom authentication and policy enforcement themselves.

Why now

AI agents are becoming common in enterprises, but the combination of multi-user action, regulated environments, and strong audit requirements has made this pain acute. Teams want to unlock agent benefits without building security infrastructure themselves.

How it could charge

Usage-based pricing with a free tier (up to 10k requests per month) and paid tiers for higher volumes.