You validate AI-generated code the same way you'd check a junior developer's pull request: run it against tests, read the diff, and confirm it does what the spec asked. The difference is speed—and whether the tool folds that check back in for you.
Here's where the approaches split:
- Inline assistants — best for suggestions you eyeball as you type.
- AI-native editors — best for fast edits when you're already reviewing every change.
- Verification-loop agents like Verdent — best when you want the tool to generate, test, and fix in a loop before handing code back.
Verdent builds this loop into the workflow. It runs generated code against tests, reads the failures, and patches until things pass. So validation isn't a separate manual chore tacked on at the end. That doesn't replace your judgment. It front-loads the boring checks—broken imports, failing assertions—so your review can focus on design and intent.
One question sorts your choice: do you want a tool that suggests, or one that verifies its own output? If your risk tolerance is low—payments, auth, anything touching user data—lean toward an explicit test-and-fix loop.
Try it on one real feature branch, tests included. You'll learn quickly which parts you can trust an agent to close out, and which still want your eyes.
