The idea is that each subagent is focused on a specific part of the problem and can use its entire context window for a more focused subtask than the overall one. So ideally the results arent conflicting, they are complimentary. And you just have a system that merges them.. likely another agent.

You just pipe it to another agent to do the reduce step (i.e. fan-in) of the mapreduce (fan-out)

It's agents all the way down.

Start with 1024 and use half the number of agents each turn to distill the final result.