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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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For running untrusted code in a multi-tenant environment, like short-lived scripts, AI-generated code, or customer-provided functions, you need a real boundary. gVisor gives you a user-space kernel boundary with good compatibility, while a microVM gives you a hardware boundary with the strongest guarantees. Either is defensible depending on your threat model and performance requirements.,详情可参考91视频

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