AI Admissibility - Control automated and AI-driven execution before action

AI Admissibility places an external allow or deny boundary before automated execution continues. Instead of relying only on post-event scanning or self-approval inside the same workflow, it moves the decision boundary outside the workflow that is asking to proceed. Public paths today include a live pilot, a commercial request path, and a private deployment path.

Built this around one simple idea: the workflow that wants to execute should not be the same place that decides whether execution may continue. Most existing tooling helps after the fact: scanning, monitoring, or post-event review. I wanted a boundary that sits before action and keeps the allow or deny decision outside the workflow that is asking to proceed. The current public surface includes: * a live pilot * a commercial request path * a private deployment path * a GitHub Marketplace action install surface The website is the main entry. The Marketplace listing is the install surface. Interested in feedback from people working on CI/CD, security controls, approval boundaries, and automated execution.

Thanks for checking out AI Admissibility.

The core idea is simple: automated or AI-driven execution should not rely only on post-event scanning or self-approval inside the same workflow.

AI Admissibility places an external boundary before action. A request is evaluated first, then the system returns ALLOW, DENY, or FAIL-CLOSED.

You can try the live pilot here:

https://ai-admissibility.com/pilot

There is also a private deployment path for teams that need stronger isolation:

https://ai-admissibility.com/private-deployment

Feedback is welcome, especially from people working with automation, AI agents, security, compliance, and execution-risk controls.

AI Admissibility - Control automated and AI-driven execution before action

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