It’s probably a legacy code quality issue too. If your code is a mess, the only fix it can find is a kludgy one. The first thing I had it do, once it was good enough to do real work, was a legacy refactor to get architecture up to best practice standards. Heck, even OpenAI’s Harness Engineering specs say that this is essential to do first. I suspect that many of the people saying it comes up with shitty solutions are the same ones that always complain about over-engineering.

You’ll also get subpar results if you just have it start coding without fully architecting its solution first. Just like a real developer.

It can’t “take over”, it’s has no sentience or actual intelligence

I’m not worried about it taking over. Describe exactly what you want, clearly is exactly the job of a programmer and why we aren’t going to be replaced by middle management wielding an army of AI.

The difference is code largely can’t be ambiguous where human language is subject to interpretation. So the question is what it takes to describe what is required in a way that the AI cannot misinterpret ambiguity, and so that agentic AI doesn’t have ambiguity decided confidently wrong by the orchestration layer.

This is not strictly a user problem. AI will confidently make bad assumptions about ambiguity and propagate those to sub agents and you wind up with garbage. I’m good at this — would never claim to be the best and maybe you are better, but I’m good at it — and it still causes me grief. One criticism I have of the whole endeavor is that proficiency seems stubbornly difficult to transfer.