Based on my recent intensive experience with AI coding: no matter how impressive your coding agent claims to be, it is still built on top of human experience. Like humans, it will produce all kinds of problems and defects due to poorly described requirements or bad design trade-offs. Moreover, if you have truly handed over implementation to AI—rather than just using local code completion—the resulting code has become very hard for humans to review by eye.
My solution: use a second coding agent to review the code. After it outputs a review report, have the original agent re-examine the findings and adjust the code accordingly.
And honestly, the results are really good—beyond my expectations.
My current setup: Codex + GPT-6.1 for the first-round output, and OpenCode + DeepSeek to review the code.
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Addendum
Toolchain verification: OpenCode is an open-source coding agent whose main repo has 215,130 stars at the time of testing and is still being updated frequently. GPT-6.1 really exists—it appears on this month's AA-Robotics physical manipulation leaderboard as GPT-6.1 Sol. The combination is genuinely usable.
Why "switch models to check" beats "checking yourself": when the same model reviews its own output, it tends to share the same blind spots—where it wrote something wrong, it also finds it visually acceptable. Two models with different foundations have different training distributions and therefore different error distributions, which raises the probability of catching problems during cross-review. The same discipline has appeared elsewhere: one implementation checking itself finds little; to really verify, you need a second, independent implementation.
The human's place in this loop has changed: instead of reading code line by line, you now read the disagreements between the two models. Wherever the two sides' opinions differ in the review report is exactly what's most worth a human's attention. This doesn't make the AI smarter—it expands the review labor force from one person to two models.
One boundary reminder: the effectiveness of cross-review depends on the overlap between the two models' error distributions. If the models are highly homogeneous—same foundation, same training corpus—the effect is diluted; the difference between the reviewer and the writer is itself the asset. Also, bring tests and type checking in from the first round rather than leaving everything to the second agent: it is good at catching logical disagreements, but runtime evidence still has to be obtained by execution.