About
We Build The Supervisor,
Not The Agent.
AgentPlane is a control layer that sits between AI agents and the systems they act on. We started it because every team giving agents real permissions was rebuilding the same allowlists, approval hacks and audit logs, and none of them could prove what their agents had done.
What we believe
Four things we will not trade away.
- 1
Agents are only useful when they can act.
A model that can only draft is a writing tool. The value arrives when it can send, change and pay, which is exactly when supervision matters.
- 2
Supervision should shrink over time.
The goal is not more approvals. It is fewer, better ones, with rules that take over as evidence builds.
- 3
Enforcement must be boring.
Deterministic rules, the same answer every time, and a record anyone can check. Models can explain; they should not decide.
- 4
Say what is not done yet.
Our trust center labels every control as in place, in progress or planned. We would rather lose a deal than overstate a control.
How we work
Small, public, measurable.
We work with a small group of design partners who already run agents against real systems. We measure review time, exception rate and false positives with them before and after, and we publish what we learn.
We are vendor neutral. AgentPlane works with any model provider and any agent framework, and we do not host or build agents ourselves.
Every change that affects how decisions are made goes in the changelog.