How do you review AI-generated code in production?

As more teams ship AI-generated code, we kept running into the same problem: traditional code review tools focus on style and lint-level issues, while the real risks are in execution flow, state handling, and silent regressions.

That’s exactly why we built CodeFox - an AI reviewer that analyzes what actually happens at runtime, not just how the code looks.

It highlights things like:

• data integrity breakages

• behavioral changes between OLD vs NEW code

• async blocking and race conditions

• real exploit and failure scenarios

Instead of leaving dozens of low-signal comments, it surfaces a few critical, production-relevant findings with deterministic fixes.

Curious how others are handling this:

👉 Do you trust AI-generated code in production today?
👉 What’s the hardest class of bugs for your current review process to catch?
👉 Would you prefer fewer but higher-impact review comments?

Site: https://code-fox.online/
GitHub: https://github.com/URLbug/CodeFox-CLI

Would love your honest feedback - we’re actively shaping the roadmap based on how teams actually ship.