Anthropic 发布指南建议公司移除不必要的 subagent，其团队发现配备 skills 的单个智能体常胜过复杂多智能体系统，有客户执行速度约提升 2x。
Anthropic 建议把不必要的 subagent 折叠进主循环，作者补充了仍需保留的场景和用 evals 决策的方法。
Banger guide by Anthropic.
While frontier models have improved at context management, subagents still help in some cases (e.g., parallelizing research tasks with their own context windows).
I still use them, but mostly for complex research work via dynamic workflows and /goal.
For most agent workflows, folding subagents into the main loop via tool calls and skills makes a ton of sense (as Anthropic recommends). That's because newer models are more persistent and better at managing large contexts. Subagents might be getting in the way now, since models can handle much of what subagents were used for. I see a similar pattern with memory (another day).
But you can't just get rid of subagents if you have been using them extensively. You need to run evals (cost, efficiency, etc.) and let that help you determine whether to fold them (and how) into the main agent loop.
The evals should always inform your strategy.
But keep in mind issues that can arise when scaling agent work, such as too many tool calls, context bloat, and unclear separation of concerns. Subagents are still very useful for that.
Lastly, all of this is constantly evolving as frontier models improve. Always keep an open mind and regularly test/eval your workflows and harnesses.
Anthropic is now recommending that companies REMOVE unnecessary subagents from their AI systems. This is a pretty significant shift in agent architecture, with their team finding that a single agent equipped with skills can frequently outperform complex multi-agent systems, and one customer seeing roughly 2x faster execution. The emerging rules of thumb: → Skills: Teach the agent HOW to do something, including procedures, expertise, and instructions. → Tools / MCP: Give the agent the ability to DO things through APIs, actions, and live data. → Subagents: Use for parallel work, isolated context, or tasks a single agent struggles with. The interesting part is progressive disclosure. Instead of spawning specialized agents or stuffing everything into a giant prompt, one agent discovers and loads skills and tools as needed. Their advice is to fold existing subagents into the main loop, turn their instructions into skills, expose their actions as tools, and run evals to see if performance holds up. As models have gotten smarter, improving agent architecture is becoming less about what you can add and more about what you can delete. https://claude.com/resources/articles/building-agents-when-to-fold-subagents-into-your-main-agent-loop在 X 查看被引用的帖子
来源：elvis · x.com