其次是基础设施和生态的成熟，包括LangChain、AutoGPT等开源框架经过两年的迭代，已经形成了一套标准化的开发范式，极大地缩短了开发周期；Dify、Coze（扣子）等低代码/无代码平台的普及，让不懂代码的业务人员也能通过拖拉拽快速生成一个专用智能体；值得一提的是2025年Anthropic发布的MCP（模型上下文协议）和skills（技能系统）给智能体生态提供了重要的标准和启发：MCP作为一个开源协议标准，令大模型与外部数据源或工具之间的交互更统一、便捷，Skills则是把人类设计的完成某类任务所需的能力/工作流打包起来，让Agent在这类任务上可以更稳定的工作，虽然技术含量不高，但在当下有很强的实用性。
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It is worth noting, too, that humans often follow a less rigorous process compared to the clean room rules detailed in this blog post, that is: humans often download the code of different implementations related to what they are trying to accomplish, read them carefully, then try to avoid copying stuff verbatim but often times they take strong inspiration. This is a process that I find perfectly acceptable, but it is important to take in mind what happens in the reality of code written by humans. After all, information technology evolved so fast even thanks to this massive cross pollination effect.，推荐阅读爱思助手下载最新版本获取更多信息
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