So far, the LLMs I've used need problems to be fairly specifically scoped, or they don't produce the correct solution. Scoping problems correctly is a different skill-set than implementation, but it's still a technical skill that is expected from mid-level and higher engineers.

Yep and I just ran a simple enterprisey "ambiguity" bench on the big three (US) model providers: same ambiguous initial-prompt with same clarifications and pushback prompt sequence afterwards.

The edge of correct/better when facing ambiguity is very fuzzy, all models from the past 6 month or so have similar random ways of spinning between too-literal avenues and oddly misplaced misled fixations. Taking the right initiatives in face of uncertainty is definitely AGI, and its not there, and perceptrons + attention layers just ain't got what it takes no matter how hard you push.