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.

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.

• OpenAI's Codex keeps one continuous thread alive via server-side compaction (auto-summarizing/filtering tool calls) to preserve coherence across huge token counts, but this serializes work through a single channel.
• Anthropic's Claude delegates subtasks to parallel sub-agents that report back to a parent thread, yielding faster visible output but risking duplicated searches and dropped facts when sub-agents fail to forward key details.
• Claude's approach costs more and risks inconsistency from repeated operations, while Codex's compaction reduces "forgetting" at the cost of speed since work happens serially.
• Both companies are expected to converge toward hybrids—OpenAI adding agent-style delegation, Anthropic tightening compression—to balance coherence, speed, and cost.