February 2026
Intermediate to advanced
406 pages
4h 57m
Chinese
Content preview from Kubernetes 上的生成式人工智能 (Chinese Edition)
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第9章 生产环境中运行智能体 应用程序
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在第8章中，我们探讨了人工智能驱动应用程序的架构模式，并在概念层面介绍了智能体工作流。 现在我们将视角从架构转向实际挑战——如何在生产环境中运行这些系统。 由于2026年的人工智能领域仍在快速演变，技术细节可能在数月内就过时。 与其罗列可能消失的框架，我们更专注于那些跨越工具与标准的持久化运维模式。 我们的目标是提供可适配任何框架的指导原则。
本章将探讨在Kubernetes上运行智能体应用的三大核心挑战：
- 安全
-
代理程序常代表用户与外部工具（ ）及数据源交互。您需要强大的身份管理、认证模式和授权控制机制，在保障用户上下文完整性的同时，允许代理程序自主运作。
- 智能体协同
-
多智能体系统需要标准化通信协议。智能体必须发现彼此的能力，跨服务边界分配任务并追踪进度。
- 状态管理
-
与无状态的REST API不同，智能体需在多次交互中维持对话上下文。生产环境部署需采用持久化存储模式，确保在Pod重启后数据不丢失并支持水平扩展。
本章涵盖两种协议 ，它们在2024年末已成为代理通信的事实标准。 模型上下文协议（MCP）规范了代理与工具间的通信，而代理间协议（A2A）则规范了代理间的协同机制。 这些并非官方标准机构制定的理论规范，但OpenAI、谷歌、微软、AWS等行业巨头及开源社区已达成共识。 2025年成立的智能体AI基金会 为这些标准化工作提供了中立平台（详见侧边栏）。
首先让我们探讨模型上下文协议，该协议为智能体提供了连接所需工具和数据源的标准化方式，从而完成工作任务。
模型上下文协议
模型上下文协议（MCP）是 推出的开放协议，可让AI驱动的智能体以一致、结构化的方式连接外部工具、数据源及服务。 该协议由Anthropic于2024年末推出，被称为"AI应用的USB-C接口"，因其解决了早期工具调用方案的集成痛点，迅速成为智能体与工具互操作性的事实标准。 在MCP出现之前，框架采用临时API调用、专有插件和无法扩展的M×N集成方案，工具间上下文传递过程脆弱且易出错。 ...
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