I find it difficult to always have one or more long-horizon tasks 'queued up' and ready to run... I find myself usually bottlenecked on design, review, or something similar that requires me being in the driver's seat. It's possible I could queue up a bunch of tasks, letting the LLM run off in multiple directions, but then I'd be less able to steer and course correct.
Just my experience though, I'm still figuring things out. Perhaps some subsets of tasks would be more ideal for these long-horizon workloads - exploration, multiple competing implementations, etc...
Use your favourite harness to help you find long-horizon tasks to have queued up. It's changed the structure of how my projects work a bit, and do you have to do some homework fast of how slow/fast your various providers or local inference are, but it's worth it. Start off with hobby projects so you get a feel for how it works.
I left something gargantuan running over the weekend (decompiling 1980s-era system software) and look forward to checking it out later today when I have a few free minutes.
In principle, a slower inference ought to be easier to steer and course-correct. You'd always be able to look at partial results, especially with a local model that doesn't hide its thinking.