Yes, this is a core part of my workflow; use the smartest model (e.g. Fable) to generate a large hierarchical implementation plan, then clear the context and have a lesser model (e.g. Sonnet) track and execute the plan [0], often in parallel.

It all depends on how much work you're doing; if it's a simple task, there's virtually no need for plans at all; just have the smart agent do it. But if the work is going to take 5, 10 or 100 sessions, there's real value in the "smart agent plans, cheap agent executes" model.

[0]: https://github.com/bensyverson/jobs

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wrs 2 hours ago | parent | next [-]

My main point was not about the implementation stage, but the verification stage. Letting the smarter agent verify the work of the cheap agent finds a lot of little oversights and sloppiness that I used to have to find myself. So I’d be interested in that cost comparison.

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ricardobeat 13 hours ago | parent | prev [-]

This is exactly what the post argues against though, as it leads to higher overall cost.

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bensyverson 7 hours ago | parent [-]

Yes, and I'm arguing that the post is over-simplifying. As I mention, for small plans, it's probably easier to have the big model just do it. But for large, multi-session plans, it's cheaper to let smaller models execute the bulk of the work.

	▲	markust 3 hours ago \| parent [-]
		Perhaps, there's a small change that might work better here - if we use a different agent (by creating custom agents) for plan and implementation, then the switch to the implementation agent wouldn't end up re-reading a lot as the new agent would have fresh empty context. I implemented this[1] from one of the posters on HN and I think it'd work well with this problem too. Would be great if you could point out if my understanding is wrong. [1]: https://www.stavros.io/posts/how-i-write-software-with-llms/