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.

• Status-check prompts that dumped full JSONL transcripts into the main thread, not parallel execution itself, were the real token/attention sink in a multi-agent Claude Code session.
• The actual bottleneck in long multi-agent runs is the orchestrator's working memory getting polluted, since tokens are a one-time cost but context pollution degrades focus for the rest of the session.
• Running adjacent subagents independently meant each paid the full cost of learning the same architecture from scratch, wasting effort that duplicate orientation could have avoided by grouping tasks with shared context.
• The fix is partitioning work by "cognitive locality" (grouping tasks that share a mental model) and having subagents return only refined conclusions rather than raw reasoning, keeping noise out of the orchestrator's context.