A test harness that blocks unsafe AI actions before execution

I built a small test harness that evaluates AI actions before they execute.

Instead of relying only on prompts or output filtering, this introduces an authorization layer that evaluates whether an AI action should be allowed before it runs.

Each requested action is analyzed for signals such as:

• financial actions • external communications • data exports • system modification • destructive operations

Based on the detected signals and required authorization layers, the harness determines whether the action should PASS or DENY.

Example output:

Running 14 tests...

[1/14] financial_commitment -> DENY [2/14] send_external_email -> DENY [3/14] deploy_to_production -> DENY [14/14] general_information -> PASS

Every evaluation produces an auditable record including:

• detected signals • required authorizations • PASS / DENY decision

The goal is to explore what a deterministic execution governance layer might look like for AI systems interacting with real environments.

Demo video walkthrough: https://www.linkedin.com/feed/update/urn:li:activity:7436787... Repository:

https://github.com/celestinestudiosllc/ai-action-authorizati...

Curious how others building agent systems or AI runtimes are approaching execution authorization.