Testing Agent Policy Compliance via CLI
Original_Iron7191 · reddit · 2026-07-17
The author is developing a lightweight CLI designed to test the decision-making accuracy of AI agents before they go live.
The core problem being addressed is that when teams modify system prompts, switch models, or update tools, an agent's decision-making behavior can subtly change, often going unnoticed until real users are impacted. Failure scenarios mentioned include:
- Approving refunds that should have been denied
- Missing support tickets that required escalation
- Approving loan applications that violated policies
The CLI works by taking a system prompt and the desired policies, running the agent through test scenarios, and verifying that its final actions comply with the rules. The author notes they caught issues during testing, such as a medical triage agent failing to advise a suspected stroke victim to seek immediate care, as well as loan approval miscalculations.
They emphasize a focus on whether the decision is correct, rather than conversational fluency. While teams like Coval and Cekura are working on voice/conversational quality, this tool is strictly focused on policy-level final action validation.
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