There are a lot of ways to slice the problem of getting the agents to complete a goal without getting lost, and the right solutions are somewhat problem-specific. For the projects I've done 35B Qwen is about the smallest that seems to make useful progress in a general purpose harness while 4B Qwen is workable with a task-specific harness. At the lower end the plan has to be traditional search/planner techniques in code not something the model has any control over, of course that limits the kinds of problems that fit. The high end coding models are perfectly capable of making a functional 1-off harness for those jobs so it ends up not being that bad to implement.
Yeah it's exactly what I've seen as well. Ordewell is close to your high end case: a frontier model builds the plan once, then it's fixed, the executing model can't reinterpret it. Won't get you to 4B, but should help a small model that only has to execute, not plan and execute at once.
Have you quantified the performance on any particular benchmark?
I thought about it but where the tool shines are large undefined tasks, which are complex to quantify and test (not as easy as implementing a simple bug fix that you can test directly). Even if I were to find such test dataset it would probably require a lot of money to reach statistically significant results. Either way, the framework is inspired a lot from Matt Pocock, and is pretty much established.
In my experience the details matter a lot. Too rigid a plan and the agents get stuck and then thrash endlessly on work they manufacture for themselves, too little they get lost, the integration strategy is in part forced by cost (e.g. using Claude Code subscription discounts by using their TUI), etc.