> Where you specify a top-level objective, it plans out those objectives, it selects a completion metric so that it knows when to finish, and iterates/reiterates over the output until completion?
I built Plandex[1], which works roughly like this. The goal (so far) is not to take you from an initial prompt to a 100% working solution in one go, but to provide tools that help you iterate your way to a 90-95% solution. You can then fill in the gaps yourself.
I think the idea of a fully autonomous AI engineer is currently mostly hype. Making that the target is good for marketing, but in practice it leads to lots of useless tire-spinning and wasted tokens. It's not a good idea, for example, to have the LLM try to debug its own output by default. It might, on a case-by-case basis, be a good idea to feed an error back to the LLM, but just as often it will be faster for the developer to do the debugging themselves.