At what point do you think you tip over from getting away with a handful of agents working on different things in one repo, to needing a comms/coordination system for your tasks?
I think there must be a burden to run this and I'm guessing it only pays off when you get to some kind of scale. What is that scale? Other human team members? Sheer workload?
[–] glennfiedler 1 day ago
[–] drphilwinder 1 day ago
I can't.see myself specifying 60 bits of work to work on in parallel. So I guess you're doing some kind of task decomposition.
But I'm not convinced that this is that much more efficient, given the complexity of the coordination overhead.
E.g. in my mental model, 3 big tasks takes about the same amount of time as 60 small tasks after planning and coordination.
What's your experience?
Edit: and all the harnesses do subagents pretty well now anyway, so dynamic parallelism when you need it.
Past a certain number of things, you can have a conversation with Claude in one session but even in Fable 5.1 or Opus 5.5 it's going to forget some and they just scroll out of context and get compacted away. Once enough things are in flight they start getting forgotten, or half-started, then the next thing is in flight and so on. When I start noticing this, I know it's time to switch over to the sprint system.
The other thing is simply I want to go really wide and work on something and it's too slow when working with traditional child agents per-model. The LLMs generally won't "work to width" naturally (for whatever reason) and the sprint system is really good at pushing and ensuring that each model involved in the sprint not only works, but works at whatever particular width they are assigned (16, 32, 64 cards...) and stays working that way.
If you work without a sprint system, you'll find yourself continually reminding the AIs manually "hey, can you please work to width?", "hey, you have 10 cards ready can you please work on them now?". These things are "squishy" when worked on in LLM space, they'll kinda do it, then a few hours later forget, and by morning they're puttering along at a few cards each.
But by putting everything into mechanical structure (sprint system that drives all the LLMs via push notifications), they will more reliably remember to work at width, they will not forget anything they were assigned to work on (this is stored in redis cards, not in their memory that compacts), and the whole system can work much wider and you get done a lot faster!