大白话系列，是希望用最通俗的文字，揭示复杂技术背后的简单本质。
文 | Jomy @302.AI
编 | 南乔River @ShowMeAI
我们 302.AI 做 MCP 和 Agent 相关开发有一段时间了，期间一直与开发者和用户们保持着密切的交流。
有一个普遍的感受：尽管行业内几乎所有人都听过 MCP、Agent 这些术语，但只有极少数人真正理解它们的本质。
今天，我就基于 302.AI 的实践和成果，分享一些自己的见解，帮大家厘清概念的来龙去脉，并大胆预测一下未来的发展方向。
LLM 的局限：缸中之脑，只能说不能做
让我们先从大语言模型（Large Language Model，LLM）说起。
大语言模型，顾名思义，就是一个只能处理和输出文字的系统。
早期的大语言模型，输出非常不稳定，准确率很低，经常「一本正经地胡说八道」。
所以，人们最多把它当成一个顾问：咨询意见，但不敢让它直接拍板决策或上手干活。
这个时期的大模型，有点像被限制在「缸」里的「大脑」（借用哲学上的「缸中之脑」假说）。
它能思考、能滔滔不绝地输出观点。但它没手没脚，不能对「缸」外的物理世界/数字世界直接做点什么。
但是，AI 技术发展飞快。
随着模型参数规模的扩大和训练方法的革新，语言模型的「智力」得到了肉眼可见的提升。人们惊喜地发现，AI 写出的文案、给到的建议、生成的代码，几乎不需要修改就能直接使用了！
眼看着 AI 越来越靠谱，一种想法自然而然地浮现出来：既然大模型这么能干，是时候解开 AI 的禁锢，让它不只能「动动嘴」，也能「动动手」了？
Function Call：大模型学会了调用工具
怎么解开 AI 的「禁锢」呢？
答案就是让大模型能够自行使用工具，也就是我们常说的 Function Call（函数调用）或 Tool Use（工具使用）。
那么，一个只会输出文字的模型，是如何调用工具的呢？
这里需要做一个必要的澄清：模型调用工具，并不是模型真的「动手」去操作工具。
本质上，还是模型生成文本（结构化的文本），然后配套的程序接收到指令，再去调用工具。（如下图所示）
而所谓的「工具」，就是各种各样的程序接口（API）或者软件操作，例如搜索、编辑数据库、编辑文件等。
gpt-4o 调用工具的命令（JSON 格式）
这就像给一个思维敏捷但行动不便的人，配备了一台随时待命的智能计算机。
他只需要「说」出来需要做什么，计算机就会自动决策和执行所有的指令，整个过程不再需要人类的介入。
让 AI 模型使用工具，本质上是一种「放权」行为。
人们将 AI 从「缸」里释放出来，允许 AI 通过调用工具，直接对现实世界或数字世界产生实际的影响。
这无疑是 AI 迈出的关键一步，也是 Agent 得以诞生的基石。
MCP 协议：统一工具接口，不再重复造轮子
AI 学会了使用工具，这很好。
但很快就出现了新问题：每家公司、每个开发者都在用自己的方式定义和接入工具。这就导致了大量的重复劳动，并且工具难以复用和共享，只能「自己造自己用」。
Anthropic 公司敏锐地发现了这个问题。他们认为，工具应该有一套通用的「语言」和「接口规范」，于是提出了 MCP（Model Context Protocol，模型上下文协议）。
（MCP 统一了模型调用工具的方式）
这个协议对大模型发展的意义重大，完全可以类比为秦始皇当年规定的「书同文」和「车同轨」。从此，模型调用工具这个事情就被大大地加速了。
MCP 是什么呢？
简单来说，它是一套定义大模型如何发现、理解和调用外部工具（或称服务）的标准协议。
（MCP 中的 Prompt 和 Resource 暂且不论）
它明确了两个核心角色：
- MCP Client（客户端）： 通常是使用工具的一方。一般是 AI 应用，比如 Claude 客户端、Cursor 编程工具等。
- MCP Server（服务端）： 也就是提供工具的一方。任何拥有 API 或软件服务的公司，都可以按照 MCP 规范把自己包装成一个 MCP Server，把原来给人用的工具，改造成能让 AI 理解和调用的工具。
（MCP基本框架示意图）
(Norah Sakal 这张「MCP architecture」图非常出圈，说的也是一回事)
前段时间，海外知名投资机构 a16z 制作了一份 MCP Market Map，梳理了 MCP 发展现状。
可以看到，MCP Client 和 MCP Server 生态已经初具模型并在日益繁荣。（如下图所示）
（MCP 生态现状）
其中，在 MCP Marketplace 板块，MCP.so 导航网站的制作者，就是国内知名的独立开发者 idoubi。
目前，MCP.so 收录的 MCP Server 数量已经超过了 7000 个，其中就包括 302.ai 的 Sandbox MCP Server和 BrowserMCPServer。
用户只需要在客户端连接这两个MCP Server，就可以让AI对远程Linux服务器和远程浏览器进行操作，完成相应的任务，极其便利。
(mcp.so 主页截图)
AI Agent：从工具调用到自主决策的进化
MCP 让工具的供给变得更加方便了。
但新的挑战接踵而至：一个 AI 模型能有效掌握和使用的工具数量是有限的。就像一个人能熟练掌握和使用的技能，也是有限的。
在人类社会里，每个人都专注于擅长的领域，以医生、律师、教师、工程师、程序员等职业身份，把自己的事情做好。
AI 也是同理。
当工具不再是主要瓶颈后，如何让 AI 模型更聪明、更高效地使用这些工具，就成了核心问题。
Agent 就在这个背景下诞生了。
(Agent 最简公式)
我认为，理解 Agent 最简单的一个公式就是：
Agent = LLM + Tools
有工具使用权限的 AI 就是 Agent，中文翻译成「代理人」，有些地方会翻译为「智能体」。
（Agent 最简单的实现框架）
现在，对于 Agent 的发展，业界有两个大的方向：
- 通用 Agent（通才）：很多大模型公司都在往这个方向努力。但现阶段，受限于模型能力等各方面的挑战，这注定暂时只是一个美好的理想。
- 垂直 Agent（专才）： 专注于解决特定领域或特定类型任务。目前看来更容易落地、也更有可能在短期内产生实际价值。
在垂直 Agent 优化实践中，我们有几条关键经验，也在此分享给大家：
- 明确的指引。在实践中体现为精准的系统提示词。
- 垂直的工具。在实践中体现为只接入任务强相关的MCP Server。
- 完整的上下文。在实践中体现为完善的任务描述和任务记忆。
读到这里，你就已经追到了 AI Agent 领域发展的最前沿。
V. Agent 通信：新的协议应运而生
然后，更新的挑战又出现了。
单个垂直 Agent 能解决特定问题，但面对更复杂的现实任务，往往需要多个不同能力的垂直 Agent 协同配合。
现在，各家公司都在闭门造自己的 Agent。这些 Agent 之间缺乏统一的沟通方式和协作机制，注定重复且低效。
这有点像 MCP 出现之前的工具生态，又一次走到了需要「标准化」的路口，只不过这次标准化的对象是 Agent 本身。
为了解决 Agent 之间的信息互通问题，一些新的协议开始进入起草阶段，其中比较受关注的有：
- ANP（Agent Network Protocol）：中国开发者率先提出并推动的一个协议。
- A2A（Agent-to-Agent）Protocol：Google 也在探索类似的概念和协议。
（Google A2A Protocol 原理示意图）
这些协议的核心目标，大致可以归纳为两点：
- 第一，让 Agent 之间明确彼此的能力，便于协作。就像外包网站的个人主页，清晰写明自己的专长，其他人可以按需查找，找到合适了的人就一起做项目。
- 第二，让 Agent 之间可以高效地传递信息。就像团队协作之前，大家约定好沟通方式（比如人会约定好用飞书还是用钉钉）以及消息格式（类似布置任务需要包含哪些信息）。
至于未来哪个协议会成为主流，现在下结论还为时尚早。
但可以肯定的是，Agent 之间的互联互通，将进一步释放 AI 潜能，催生一个更加靠近 C 端（用户端）、更加繁荣、更加有想象力的巨大市场。
Agent 的未来：通用 Agent vs 垂直 Agent，谁更有价值？
最后，让我们来系统回顾一下这条演进路径：
- 随着大模型能力的提升，AI 输出的内容越来越靠谱。人们开始让 AI 通过调用工具与外部世界直接交互，把 AI 变成了 Agent。
- MCP 协议统一了工具的开发标准，从而简化了 Agent 的开发难度。
- 现阶段单个 Agent 无法很好地使用大量工具，所以垂直 Agent 成为了当前的主流。
- 垂直 Agent 之间需要配合才可以完成复杂的任务，这催生了 ANP / A2A 等新的互联协议。
你看，技术的发展往往不是一蹴而就的。
每个重大节点（Function Call/Tool Use、Agent、MCP、ANP/A2A）的出现，背后都有其历史必然性。
再往后呢？
考虑到当前基础模型的发展速度和架构特点，我认为，垂直 Agent 市场将会在历史舞台上存在相当长的一段时间。
即使未来出现了能力极强的「超级通用 Agent」，在很多场景下，高度优化、成本可控的垂直 Agent 组合，可能仍然是更具性价比、更可靠的选择。
恰似古语有云：三个臭皮匠，顶一个诸葛亮。
Agent 生态的爆发，可能来得比大多数人想象中还要快。
2025 是名副其实的 Agent 之年。这背后蕴藏着的，是巨大的技术变革和商业机会，以及我们这代人几十年才得一遇的科技浪潮。
衷心希望我今天的分享，能帮你更好地理解这个正在发生的未来。也希望每一位在这个领域努力探索的朋友，都能抓住机遇，在今年摘取到属于自己的胜利果实。
常见问题 FAQ
Q1：什么是 AI Agent？和 LLM 有什么区别？
AI Agent 的核心公式是：Agent = LLM + Tools。和 LLM 的核心区别：1）LLM 是”缸中之脑”——只能处理和输出文字，没有手脚，不能对现实世界直接做点什么；2）Agent 是”大脑+手脚”——LLM 负责思考，Tools 负责执行，可以调用外部工具、访问 API、读写文件、与系统交互；3）LLM 是被动响应，用户问一句答一句；Agent 是主动执行，可以自主规划任务、调用工具、完成复杂工作；4）LLM 像”顾问”，只能给建议；Agent 像”员工”，可以实际执行任务。简单说：LLM 是 Agent 的组成部分，但单独的 LLM 不是 Agent。
Q2：什么是 Function Call（工具调用）？LLM 是如何调用工具的？
Function Call（函数调用）或 Tool Use（工具使用）是让大模型能够自行使用工具的机制。需要澄清的是：模型调用工具，并不是模型真的”动手”去操作工具。本质上是：1）模型生成结构化文本（通常是 JSON 格式），描述需要调用什么工具、传入什么参数；2）配套的程序接收到指令，再去调用实际的工具（API 或软件操作）；3）工具执行后返回结果，模型再根据结果继续推理。这就像给一个思维敏捷但行动不便的人，配备了一台随时待命的智能计算机。他只需要”说”出来需要做什么，计算机就会自动决策和执行所有的指令。让 AI 模型使用工具，本质上是一种”放权”行为，也是 Agent 得以诞生的基石。
Q3：什么是 MCP？为什么需要 MCP？
MCP（Model Context Protocol）是 Anthropic 推出的开放协议，统一了 LLM 调用工具的接口标准。为什么需要 MCP：1）MCP 之前——每家公司、每个开发者都在用自己的方式定义和接入工具，导致大量重复劳动，工具难以复用和共享，只能”自己造自己用”；2）MCP 之后——所有工具都遵循统一标准，就像 USB 统一了设备接口，MCP 统一了工具接口，开发者可以”一次开发，到处使用”；3）MCP 是 Agent 的基础设施——让 Agent 可以更容易地扩展能力，调用各种工具。简单说：MCP 解决了”重复造轮子”的问题，是 Agent 生态的重要推动力。
Q4：Agent 和 Workflow 有什么区别？
Agent 和 Workflow 的核心区别：1）Workflow 是固定流程——A→B→C，每一步都预设好，适合标准化、重复性任务，可控性强但缺乏灵活性；2）Agent 是动态执行——LLM 根据情况自主决策，适合复杂、需要判断的任务，灵活性强但可控性弱；3）Workflow 像”操作手册”，按步骤执行；Agent 像”专家经验”，按情况决策；4）实际应用中，很多系统是 Workflow + Agent 的混合体——用 Workflow 处理标准化部分，用 Agent 处理需要判断的部分。简单说：Workflow 适合”确定性任务”，Agent 适合”不确定性任务”，两者结合才是最佳实践。
Q5：从 LLM 到 Agent，AI 经历了哪些进化阶段？
从 LLM 到 Agent，AI 经历了 4 个关键进化阶段：1）LLM 阶段——”缸中之脑”，只能处理和输出文字，经常”一本正经地胡说八道”，只能当顾问；2）Function Call 阶段——大模型学会了调用工具，可以”动动手”，但每家公司都在用自己的方式定义工具，重复造轮子；3）MCP 阶段——Anthropic 推出 MCP 协议，统一了工具接口标准，工具可以复用和共享；4）Agent 阶段——LLM + Tools + MCP，AI 从”工具调用”进化到”自主决策”，可以独立完成任务。每个阶段都是在前一个阶段的基础上进化，最终形成了今天的 AI Agent。
Q6：通用 Agent 和垂直 Agent 哪个更有价值？
根据 302.AI 的实践经验，垂直 Agent 更有价值：1）通用 Agent 难以实现——需要处理各种领域的任务，对 LLM 的能力要求极高，目前技术还达不到；2）垂直 Agent 更容易落地——专注特定领域（如编程、设计、写作、数据分析），可以深度优化，效果更好；3）垂直 Agent 的护城河更深——积累了领域特定的经验、数据、最佳实践，难以被复制；4）通用 Agent 可能是未来方向，但现阶段垂直 Agent 更实用。简单说：不要追求”万能 Agent”，而是打造”专家 Agent”，在特定领域做到极致。
Q7：如何开始使用 AI Agent？有哪些工具推荐？
开始使用 AI Agent 的 3 个步骤：1）选择合适的 Agent 工具——Claude Code（Anthropic 官方）、Codex（OpenAI）、OpenClaw（开源）、302.AI 客户端（国内友好）；2）学习 Agent 的核心概念——理解 Agent = LLM + Tools，了解 MCP、Skills 等关键概念；3）从简单任务开始——先用 Agent 完成简单任务（如代码生成、文件整理），逐步过渡到复杂任务。302.AI 客户端的优势：1）预装了常用 Tools 和 Skills，开箱即用；2）支持多种 LLM（Claude、GPT、Gemini等）；3）云端沙盒环境，无需本地安装；4）国内访问友好，无需翻墙。
Q8：AI Agent 的未来发展趋势是什么？
AI Agent 的未来发展趋势：1）Skills 生态爆发——就像 APP 改变了智能手机，Skills 将改变 Agent，出现大量专业化的 Skills；2）多 Agent 协作——单个 Agent 能力有限，多个 Agent 协作可以完成更复杂的任务；3）Agent 即服务（AaaS）——Agent 将成为一种服务，用户按需调用，无需自己搭建；4）垂直 Agent 主导——通用 Agent 难以实现，垂直 Agent 将在各自领域深耕；5）Agent 安全与可控——随着 Agent 能力增强，如何确保 Agent 安全、可控将成为重要议题。简单说：Agent 正在从”技术概念”进化为”生产力工具”，将深刻改变未来的工作方式。
官方网站
302.AI : https://302.ai
MCP.so: https://mcp.so
Anthropic | Introducing the Model Context Protocol: https://www.anthropic.com/news/model-context-protocol
Anthropic | Model Context Protocol: https://modelcontextprotocol.io/introduction
ANP | Agent Network Protocol: https://agent-network-protocol.com
ANP | AgentNetworkProtocol : https://github.com/agent-network-protocol/AgentNetworkProtocol
A2A | A2A Protocol: https://google.github.io/A2A
A2A | Agent2Agent Protocol: https://github.com/google/A2A
推荐阅读
Norah Sakal | What is Model Context Protocol (MCP)? How it simplifies AI integrations compared to APIs: https://norahsakal.com/blog/mcp-vs-api-model-context-protocol-explained
a16z | A Deep Dive Into MCP and the Future of AI Tooling: https://a16z.com/a-deep-dive-into-mcp-and-the-future-of-ai-tooling
What is Model Context Protocol (MCP): Explained: https://composio.dev/blog/what-is-model-context-protocol-mcp-explained
What Is the Model Context Protocol (MCP) and How It Works: https://www.descope.com/learn/post/mcp#llm-isolation-&-the-nxm-problem
常高伟 | 多角度全面对比Google最新的A2A、ANP、MCP: https://mp.weixin.qq.com/s/n1R-wOtNBTKtHdIHOUhQmw
idoubi | 详解 MCP 核心架构: https://mp.weixin.qq.com/s/uTsr06MnJ9t3sGDzLD99_g
评论列表（67条）
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[…] 上一篇文章 聊了聊 Tool、MCP 和 Agent 三者之间的关系。简单来说就是 Agent = LLM + Tools，而 MCP 统一了 Tools 开发和使用的过程。 […]
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