AI Methodologies: Verification is Important

A Practical Method: Build Verification Into the Loop

Agent output is a first draft, not a conclusion. In many cases, the fastest way to trust it is to attach clear acceptance criteria and have the agent produce evidence: automated checks, validation results, and targeted verification. This approach keeps iteration cheap, makes failures obvious, and preserves human accountability with artifacts you can review.

Habits:

• Add explicit acceptance criteria to agent prompts (inputs, expected behavior, and “done” conditions).
• Require the agent to run automated checks and report results (or explain constraints when it can’t).
• Make verification checks the default for changes; have the agent add or update checks alongside the work.
• For behavior that spans components, use integration or end-to-end checks where useful (smoke tests beat vibes).
• Ask for a short verification summary: what was checked, what passed/failed, and what remains unverified.
• Keep cost bounds: cap iterations/compute, and escalate to human review when the loop isn’t converging.