From what I can tell, subagents are used to paper over poor context and cache management. In my local model constrained environment, figuring out a rolling compaction and eviction procedure in the optimum context size seems the best goal for the agent.

Once I've got that, then likely there's just going to be an infinite agent loop that adapts to whatever the current needs are.

So far I've gotten up to about 6 million tokens in a constrained max 128k window before something shits the bed. Still working on it.

Subagents have some place still, but I think you're right in that they're papering over a tool use problem. For larger labs, they're primarily a resource reduction, where smaller context models can be tied to equipment with a reverse proxy load balancer. For large context agents, you can't use a load balancer, your context must live on a single hardware device.

So while I agree you can optimize a single agent, what's forcing subagents at the cloud providers is the reality of load balancing.