Agentic Verification
Elevate
An author argues that AI agents produce higher-quality code when they can independently verify their output via automated checks that form self-running feedback loops. The single concrete point is that the core test loop should target subsecond runtimes. As a result, teams shift from humans manually reviewing diffs toward agents running tests themselves, expanding toward end-to-end, property-based, and differential checks with incentives tuned to avoid flakiness and slow feedback.
Why it matters
Giving AI agents a way to independently verify their work can significantly improve output quality. It recommends using end-to-end tests as ground truth, property-based testing for “never happen” constraints, and making tests fast and deterministic so agents can retry when conditions are unreliable.