Amazon 论文在 13 个企业 SOP 上受控比较 4 种智能体架构、8 个变体(模型、任务和工具固定),结论是企业工作流没有普适最佳架构。多智能体或技能方案消耗单智能体 1.4x 至 4.7x 的 token,收益通常很小;最初的多智能体版本任务成功率 58.2%,低于单智能体的 67.4%。建议从单智能体起步,按工作流测量成功率,先修复实现问题再考虑更复杂架构。
New Amazon paper finds that for enterprise workflows, a single agent is usually the best default, because multi-agent setups cost far more for small gains.
Teams automating long, branching business procedures can pick a single agent, an agent with skills, an orchestrator with workers, or a swarm of specialists. Little controlled evidence exists for choosing among them.
They ran 4 agent designs, as 8 variants, on Claude Opus 4.8 across 13 enterprise procedures. Tasks and mock tools stayed fixed, so only the architecture changed.
Every multi-agent or skill setup used 1.4x to 4.7x the tokens of the single agent, mostly for small gains. A first multi-agent build lost to the single agent, 58.2% to 67.4% task success, while a repaired version won but cost the most tokens.
Start with a single agent, measure success per workflow, and fix implementation bugs before paying for a more complex architecture.
来源:Rohan Paul · x.com