The title is very fashionable at the moment. Subagents are good at the part that can be broken down into separate tasks. The problem is that each subagent has its own understanding of the plan. When the plan changes, or if one subagent changes its assumptions, the windows don't stay in sync. You get work that's similar but doesn't fit together, and this shows up when you're integrating. The easy part is parallelism. The difficult part is making sure the different agents work well together, and that's not something you can easily achieve by increasing the number of subagents. I'm curious to know if your seven were reading from one source that stayed fixed, or if any of them were supposed to react to what the others produced.