How to test an AI agent policy before enforcing it

As organizations scale AI agents, reliable authorization testing is needed to ensure agents can complete assigned work without exceeding user access, task-specific intent, or organizational policies. The guide proposes building repeatable test fixtures for workflows such as summarizing Linear issues and sending an email, recording the actor, authorized task, proposed action, policy version, expected authorization decision, and resulting external system state. It recommends beginning with allowed cases, then changing one variable at a time to test boundaries, including draft-versus-send permissions, role-based project access, restrictions on credentials or financial data, and protections against deleting received messages. Approval workflows should be tested end to end, including pending, approved, rejected, and modified requests, to confirm that approval applies only to the exact action reviewed. Crucially, a denial log alone is insufficient: tests must verify that prohibited actions did not occur in connected systems, while allowed decisions should be distinguished from delivery or provider failures. The approach also calls for testing dependency failures, retries, evaluator timeouts, and repeated model judgments, restoring account state between runs, and tracking both unsafe actions that executed and legitimate work that was blocked so policies can become safer and more useful over time.

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