elvis 转发并评论 Anthropic 关于减少不必要子智能体的指南，认为对多数智能体工作流而言，用工具调用和技能把子智能体折叠进主循环是合理的，因为新模型更持久、更擅长管理大上下文。他仍会在复杂研究任务中通过动态工作流和 /goal 使用子智能体，并强调不能直接删除已有子智能体，而应通过成本、效率等评测决定是否折叠，同时留意工具调用过多、上下文膨胀和职责边界不清等问题。
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