While I agree that the harness is part of it, I think it's also a lack of epistemic understanding or awareness for what it means to actually solve a problem vs just get something kinda working; maybe if Claude Code or other harnesses made web search more likely or had a better way to make technical documentation and specs available to models, it would be better solvable there.

I often tell it to stop asking me and just keep going until it accomplishes X task; unfortunately it tends to assume I want something that only just barely works, in the sense that it means it's time to stop once its there, which is I don't think a harness by itself could easily address (ultimately the model itself needs to determine the stopping points unless I literally specify by hand hidden evaluation criteria).

That's why think it's at least partially a training issue where the model gets rewarded for "solving" the problem within a certain amount of context/time without access to grounded knowledge (eg looking up the actual spec for a format) nor adversarially/rigorously evaluated against a reviewer capable of finding all the edge cases/shortcuts preventing something from being a properly generalized solution. I don't want it to ask me for guidance when it's working on a well-specified problem, I want it to either find the right parser and use it, or to completely implement one against the spec, rather than write some half-assed string inserter that eg only works on the specific select statements my examples use right now. My understanding is that the Mythos/Fable models were better trained for this but from my brief foray into using Fable for work I wasn't that impressed. For me they need to get better at agentic search and self-eval still