

Maybe it’s just because Anthropic is so many more miles ahead than OpenAI, but I have not had the “it doesn’t work” experience that people are talking about here. I’ve set up agentic pipelines with code architecture guidelines, the orchestrator calls the implementer, tester, reviewer when needed. If the developed code isn’t up to standard or the work item isn’t fully captured, it’ll reject it and send it back to implementation. It invokes the architect if it runs into something unanticipated. The pipeline can run a feature with high accuracy and very little input.
I’ve even used it to run batteries of frozen experiments on self synthesized data using its own simulation harnesses to discover novel techniques that work in the real world. If it isn’t helpful, I feel like you are doing it wrong.




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.