An AI agent completes its task in staging. But can it access another customer’s records, follow instructions hidden in a retrieved document, or take an action without approval?
Before shipping, define which failures should stop the release.
For a support agent, that might mean:
- Accessing records outside the signed-in customer’s account.
- Following instructions in retrieved content that lead to unauthorized behavior.
- Issuing a refund without the required approval.
Test those boundaries against the connected workflow using controlled data. Check the outcome as well as the response: “I didn’t issue a refund” means little if the backend created one.
When you can’t verify a critical action, mark it for review. Keep the evidence, fix reproducible failures, and rerun those cases with the next candidate.
That’s the release checkpoint we’re building AI Release Assurance around: helping teams turn observed behavior into a decision to approve, block, or review a release.
Read the full ADLC article on PromptBrake, or follow AI Release Assurance on LinkedIn for more practical notes.
What failure would stop your team from shipping an agent?
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