Martin Fowler shows that in long-running multi-agent workflows the real cost isn’t parallel execution but context pollution in the orchestrator’s working memory. He recounts how status polling and redundant file orientation dumped massive transcripts into the main thread, competing for attention. He then proposes simple rules—like merging overlapping tasks and avoiding unnecessary polls—to keep only essential information in context.

This article shows how solving complex problems benefits from a team of AI agents with roles like planner, doer, tool operator, critic, supervisor, and presenter. It breaks down each subagent’s function and gives tips on prompting, model choice, tuning, and context setup. The CDN-Folk case illustrates how a team of agents designed, validated, and deployed a content delivery network faster than traditional methods.