How to Review AI-Generated Code with Multiple AI Agents
SMRTR summary
Something surprising happened when a developer asked one AI to review code written by a different AI: the reviewer couldn't tell that the final product was simply wrong.
Not broken. Not buggy. Just... not what was asked for.
This is the quiet problem emerging in AI-assisted software development. Developers are increasingly using models from different companies to check each other's work, reasoning that separate training means separate blind spots. But a reviewer that only sees the final code misses the original intent entirely.
In one telling example, a developer asked an AI agent to build two ad cards for two games. The agent built three. The code was clean. The tests passed. A standard review flagged nothing.
The fix, it turns out, isn't just about which AI does the reviewing. It's about giving the reviewer the full story: the original prompts, the decisions made along the way, the reasoning behind the result.
Without that, you're not reviewing the work. You're just admiring the destination.
SMRTR provides this summary for quick context. The original article belongs to Daily.dev.
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