Comment by all2

I think routing should be pushed 'down the stack' so to speak.

What I mean is that most tasks can be recursively fragmented into smaller tasks, and once you've hit suitable leaf nodes -- where the task is very granular -- you can begin to deterministically show which models perform better or worse for that specific task. Then, when your agent is running a workflow, you may use various models for different steps in a workflow. For example, some models may excel at exploration, some at determining a good architectural fit for an implementation, some at actually writing the implementation, and so on.

But you don't know until you define your 'work' taxonomy, and still further, you won't know until you have a statistically significant number of runs on a given chunk of work. Once you have that, though, you can hone in on models that excel at one specific task or another and prefer those the majority of the time (say ~80%) and hold back the remainder work as a 'test corpus' just in case a different or new model does even better.

This is something I've kept in the back of my head as I've been working through my agent harness primitives -- specifically enabling different models per chunk of work.