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.

No it's not because cost is much lower. They do some kind of speculative decoding in monte-carlo way If I had to guess as humans do it this way is my hunch. What I mean it's kinda the way you describe but much more efficient.

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.

Claude Cowork does this by default and you can see how exactly it is coordinating them etc.

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