I feel like this is really due to the harness.

Gemini CLI at work has the same issue: it'll prefer hacking your workstation over just asking you how to proceed.

I think the harnesses are setup to have a bias to action otherwise the LLM would just stop all the time when doing trivial task but it also mean they'll keep going when the "obvious" path is to just prompt the user.

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