In my experience it's been great to have LLMs for narrowly-scoped tasks, things I know how I'd implement (or at least start implementing) but that would be tedi

In my experience it's been great to have LLMs for narrowly-scoped tasks, things I know how I'd implement (or at least start implementing) but that would be tedious to manually do, prompting it with increasingly higher complexity does work better than I expected for these narrow tasks. Whenever I've attempted to actually do the whole "agentic coding" by giving it a complex task, breaking it down in sub-tasks, loading up context, reworking the plan file when something goes awry, trying again, etc. it hasn't a single fucking time done the thing it was supposed to do to completion, requiring a lot of manual reviewing, backtracking, nudging, it becomes more exhausting than just doing most of the work myself, and pushing the LLM to do the tedious work. It does work sometimes to use for analysis, and asking it to suggest changes with the reasoning but not implement them, since most times when I let it try to implement its broad suggestions it went haywire, requiring me to pull back, and restart. There's a fine line to walk, and I only see comments on the extremes online, it's either "I let 80 agents running and they build my whole company's code" or "they fail miserably on every task harder than a CRUD". I tend to not believe in either extreme, at least not for the kinds of projects I work on which require more context than I could ever fit properly beforehand to these robots.