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.

The article compares OpenAI’s Codex “Oracle” approach—using server-side compaction to maintain a single coherent thread—with Anthropic’s Claude “Firm” method of delegating tasks to multiple sub-agents. It breaks down trade-offs in cost, speed, coherence, and memory loss, and predicts a future hybrid of both strategies.