Show HN: A test harness that blocks unsafe AI actions before execution
A test harness that implements a pre-execution authorization layer for AI actions, analyzing requests for sensitive signals (financial operations, external communications, data exports, system modifications) and determining PASS/DENY based on required authorization levels with auditable logging.
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.