Anthropic 称 1 AI agent击败了许多子剂，现在模型可以单独处理复杂问题。
关键点
- Anthropic建议各公司评价它们是否能够将子agents合并成一个按需要加载上下文的单一agent.
- 公司的经验显示，许多专业的子agents可以导致更高的耐久性，成本上升，每次交接时都会丢失信息。
- Anthropic在GitHub上发布了一个迁移工具包来帮助团队实现过渡。
Anthropic发现，单个有点播上下文的agent在质量，成本，和耐久性上往往比多agent设置要好。 该公司正在发布一个迁移包，以帮助团队进行评估和切换。
Anthropic正建议公司评价它们能否将子agents进行整合，因为一个单一的主干agent根据需要加载上下文，往往比一支专业的车队产生更好的效果。 从Opus 4.5开始的更新型的模型，同Opus 5.5和Fable 5.1一起，可以直接在一个循环中管理复杂的计划，这也能够降低成本和等待时间。 在一次比较中，Anthropic 称的单agent架构运行速度约为多agent设置速度的两倍。
推荐来自多个企业部署的亲身体验，Anthropic的应用AI团队发现，一个使用所謂技能的agent经常会从质量上同时击败单体即时设计和子agent设置。 例如，一家公司在其客户面前的agent内部有十几个专业的子agents，这使其难以保持一致的品牌经验，以高的耐用性来伤害可用性。
这个模型现在可以做很多管弦乐工作
Anthropic表示，许多agent的设计遵循了熟悉的意向检测模式，固定的处理管道，用户分块，单独的推荐分支，以及坐在上方的管弦乐手。 大部分工作是这个模型现在可以自行处理，供各自使用。
Anthropic称"康威定律"为agents. 建筑成为了Org图表的复制品，每支队伍自行运输了子agent，直到上百个重叠的agents堆积而无共享标准。 接下来是Anthropic所称"弗兰克普伦普特"，每支球队新增规则到系统时会提示最终相矛盾。 据该公司称，一个清洁的系统即时，四到五个精益数据工具，以及固定的输出护栏，覆盖了各自的大部分使用案例。
例如，Anthropic将Claude Code系统对更新型模型的及时性削减了80%以上，部分原因是技术和工具现在只有在需要的时候才加载。 更新型的模型也对自己的进步进行评论并检查自己的作品，这可能会使一些额外的控制层变得没有必要。
Anthropic 称子-agents在单个agent的上下文窗口显示可测量性能退化，多个任务需要与单独的上下文windows并行运行，或者单个agent与工具选择相搏时仍然有意义。
为分步迁移，Anthropic在GitHub上发布了一个迁移工具包，让团队通过一个工作流程来识别现有模式，评价替代品，并验证最佳方法。 Anthropic 警告反对盲目地合并，因为在一次测试中，有支持的agent，在子agent中折叠确实会减少延迟，但会打破之前的工作过程。 初级agent开始自己回答，而不是咨询合适的专家。
团队应防止技能库扩展
Anthropic不给每个团队自己的agent，而是通过工具搜索，将包装工作流程推荐成技能并按需加载工具。 中央小队应拥有迅速和总体质量的系统。 另外，该模型工具选择的Anthropic 指出往往会降解出超过30到50个工具定义，因此每个团队中指定的所有者应当维护技能库来防止它们被气球所打出。
对于指南，产品规格等参考材料或计量定义，Anthropic推荐知识库，只有需要时，agent通过搜索工具查询。 这种信息也可以作为参考文件嵌入到技能中去。 Anthropic建议不要通过代码取回上下文，直接将上下文注入提示。
Anthropic的研究成果与近期的研究一致。 瓦尔斯的一项研究发现，agent团队可以比单个agents更快，但不能提供更高的质量。 根据这项研究，从10个增加到30个或100个agents没有带来质量收益，但成本要高得多。 OpenAI开发商Eric Provencher也对agent swarms的价值提出质疑，称其"几乎肯定"浪费了钱。
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Anthropic says one AI agent beats many subagents now that models can handle complexity alone
Key Points
- Anthropic recommends that companies evaluate whether they can consolidate sub-agents into a single agent that loads context on demand.
- The company's experience shows that many specialized sub-agents can lead to higher latency, rising costs, and information loss at every handoff.
- Anthropic has published a migration kit on GitHub to help teams make the transition.
Anthropic found that a single agent with on-demand context often outperforms multi-agent setups in quality, cost, and latency. The company is releasing a migration kit to help teams evaluate and make the switch.
Anthropic is recommending that companies evaluate whether they can consolidate sub-agents, because a single primary agent that loads context as needed often delivers better results than a fleet of specialized ones. Newer models starting with Opus 4.5, along with Opus 5.5 and Fable 5.1, can manage complex plans directly in a single loop, which can also reduce costs and wait times. In one comparison, Anthropic says the single-agent architecture ran about twice as fast as the multi-agent setup.
The recommendation comes from hands-on experience across multiple enterprise deployments, where Anthropic's Applied AI team found that a single agent using so-called skills frequently beat both monolithic prompt designs and sub-agent setups on quality. One company, for example, had a dozen specialized sub-agents inside its customer-facing agent, which made it hard to keep a consistent brand experience and hurt usability with high latency.
The model can now do much of the orchestration work itself
According to Anthropic, many agent designs follow a familiar pattern with intent detection, a fixed processing pipeline, user segmentation, separate recommendation branches, and an orchestrator sitting on top. Much of that is work the model can now handle on its own for the respective use case.
Anthropic calls it "Conway's Law" for agents. The architecture becomes a copy of the org chart, with each team shipping its own sub-agent until hundreds of overlapping agents pile up with no shared standards. What follows is what Anthropic calls a "Frankenprompt," where every team adds rules to the system prompt that end up contradicting each other. A clean system prompt, four or five lean data tools, and fixed output guardrails cover much of the respective use case, according to the company.
As one example, Anthropic cut the Claude Code system prompt for newer models by over 80 percent, partly because skills and tools now load only when needed. Newer models also comment on their own progress and check their own work, which can make some of the extra control layers unnecessary.
Anthropic says sub-agents still make sense when a single agent's context window shows measurable performance degradation, when multiple tasks need to run in parallel with separate context windows, or when a single agent struggles with tool selection.
For a step-by-step migration, Anthropic has published a migration kit on GitHub that walks teams through a workflow to identify existing patterns, evaluate alternatives, and verify the best approach. Anthropic warns against merging blindly, since in one test with a support agent, folding in a sub-agent did reduce latency but broke a previously working process. The primary agent started answering on its own instead of consulting the right specialist.
Teams should keep skill libraries from sprawling
Instead of giving every team its own agent, Anthropic recommends packaging workflows into skills and loading tools on demand through a tool search. A small central team should own the system prompt and overall quality. Separately, Anthropic notes that the model's tool selection tends to degrade beyond 30 to 50 tool definitions, so designated owners on each team should maintain the skill libraries to keep them from ballooning.
For reference material like guidelines, product specs, or metric definitions, Anthropic recommends knowledge bases that the agent queries only when needed through a search tool. That kind of information can also be embedded as reference files inside a skill. Anthropic advises against fetching context through code and injecting it directly into prompts.
Anthropic's findings are consistent with recent research. A study by Vals found that agent teams can be faster than single agents but don't deliver higher quality. Scaling from ten to 30 or 100 agents brought no quality gains but significantly higher costs, according to the study. OpenAI developer Eric Provencher has also questioned the value of agent swarms, calling them "almost certainly" wasted money.
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