{
  "schema_version": 1,
  "credential_free": true,
  "attempts": [
    {
      "attempt": 1,
      "request": {
        "model": "doubao-seed-1-6-250615",
        "messages": [
          {
            "role": "user",
            "content": "You are an exacting bilingual technical-book translation evaluator. Compare two anonymous Chinese translations against the complete English Markdown source. Score both X and Y from 1 to 5 on exactly: accuracy (no omissions, inventions, or changed claims); fluency; terminology (consistent and technically correct); markdown_code_fidelity (figures, links, headings, equations, and fenced code preserved). Each score needs concrete quoted or located evidence. Prefer one only when evidence supports it. Return JSON only: {\"variants\":{\"X\":{\"accuracy\":{\"score\":1,\"evidence\":\"...\"},\"fluency\":{\"score\":1,\"evidence\":\"...\"},\"terminology\":{\"score\":1,\"evidence\":\"...\"},\"markdown_code_fidelity\":{\"score\":1,\"evidence\":\"...\"}},\"Y\":{\"accuracy\":{\"score\":1,\"evidence\":\"...\"},\"fluency\":{\"score\":1,\"evidence\":\"...\"},\"terminology\":{\"score\":1,\"evidence\":\"...\"},\"markdown_code_fidelity\":{\"score\":1,\"evidence\":\"...\"}}},\"preferred\":\"X|Y|tie\",\"preference_evidence\":\"...\"}.\n\nCOMPLETE ENGLISH SOURCE:\n# Getting Started with AI Agents\n\nIf you have used Cursor to write code and watched it search your codebase, edit multiple files, and rerun tests until they pass, you have already used an AI Agent. The same is true if you have used Deep Research to investigate a topic through repeated searching and reading, had Manus control a browser to finish online tasks, asked the Doubao phone assistant to book tickets or send messages, or sent Pine AI to negotiate a lower telecom bill.\n\nThese products take many forms, but they share a common trait: they are no longer passive \"you ask, it answers\" conversations. They plan their own execution steps, call the tools each task requires, and adjust their strategy as results come in. AI Agents are becoming a new way to interact with computers.\n\nThis chapter begins with practical examples and works back toward the core components of an AI Agent: readers will experience firsthand what modern Agents can do, understand the architecture behind them, and learn the design patterns and best practices for building Agent systems.\n\n> **Reading Tip**: This chapter is the conceptual map for the whole book: a concise tour of the core formula, operating loop, engineering framework, and Agent design patterns. It establishes the shared vocabulary and reference points used throughout later chapters. Do not try to memorize every concept on your first read; aim for the big picture. Each later chapter expands on one aspect introduced here, and you can return to this chapter whenever you need to reorient.\n\n## Modern Agent = LLM + Context + Tools\n\nThe essence of a modern Agent system fits into one concise formula: **Agent = LLM (Large Language Model) + Context + Tools**. The formula is simple and practical—provided each term is read broadly:\n\n- **The LLM is the Agent's reasoning engine**: It is more than a set of model parameters; it is the Agent's decision-making core, responsible for understanding intent, reasoning, planning, and judgment. An LLM's capabilities come from world knowledge and language ability acquired during **pre-training**, plus decision-making strategies encoded through **post-training** (techniques such as supervised fine-tuning and reinforcement learning are covered in Chapter 7).\n- **Context is the Agent's working set of information**: Not just the text fed into the model, but the working set of information available to the Agent at each decision point—the environment, user memory, domain knowledge, its own state, and task progress. Just as a person making a decision needs to size up the situation, recall relevant experience, and consult references, the Agent's context window contains the information it can use at that moment.\n- **Tools are the Agent's action interfaces**: Not a handful of callable API functions, but the full set of ways the Agent can act—from predefined tool calls to Skills loaded on demand, from generating code to create new capabilities on the fly to delegating work to sub-agents, from reaching out to the user to responding to external events.\n\nPut more intuitively: **Agent = Reasoning Engine + Working Context + Action Interfaces**. The model reasons and decides, the context provides the working set of information those decisions depend on, and the tools provide the interfaces through which decisions affect the outside world.\n\nThese three components correspond exactly to three core concepts in RL (see Chapter 7). The following table is **optional reading**—if you do not have an RL background, feel free to skip it; nothing later depends on it. It exists only to help readers who do know RL map that knowledge onto this book's terminology:\n\n| Intuition | Agent Component | RL Concept (Optional) | Role |\n|---------------|----------------|------------------|---------------------------------------------|\n| **Reasoning Engine** | LLM | **Policy** | The decision-making logic that determines \"what to do next\"—given the current information, choose the most appropriate action from all available options |\n| **Working Context** | Context | **Observation Space** | All the information available to the Agent—what it can observe, read, remember, and which systems it can access |\n| **Action Interfaces** | Tools | **Action Space** | The complete set of things the Agent can do—what \"means\" are available, from sending messages to executing code to controlling interfaces |\n\n### Observation and Action Spaces: The Interface Between Model and World\n\nIn their classic textbook *Computer Architecture: A Quantitative Approach*, Hennessy and Patterson open Chapter 1 by asking, “What Is Computer Architecture?” and identify the **instruction set architecture** (ISA) as the interface between software and hardware[^ch1-agent-interface]. This perspective gives us a useful way to understand Agents: **the observation space and action space together form the interface between the LLM and its external environment**. The observation space translates information in the environment into context the model can process; the action space translates model decisions into operations on the outside world. Information outside the observation space effectively does not exist for the model. An operation outside the action space remains something the model can only recommend in words, even if it knows exactly what should be done.\n\nConsequently, **once the underlying model is held constant, the primary systems-engineering lever for improving Agent performance is often to redefine or expand its observation and action spaces**. In this book's terminology, that means expanding context and tools. Many problems that appear to require a “smarter model” are really interface problems: bring the task-relevant data into context or expose the required operation as a tool, and a previously unsolvable task may become solvable without retraining the model.\n\n**Manus: merging spaces that had been separate.** Before Manus appeared, production Agents mostly followed three distinct tracks: Deep Research, Coding, and Computer Use. Manus was the first widely influential production Agent to bring all three together in one system. The web enlarged its observation space; the file system and code execution enlarged its action space; and screen perception together with clicking and typing brought graphical interfaces into both. Manus did not become a general Agent merely by swapping in a stronger model. It took the union of three kinds of Agents' observation and action spaces, enabling one Agent to cross the previous product boundaries.\n\n**OpenClaw: extending the interface into the user's digital life.** OpenClaw pushes both spaces outward again. It receives tasks and returns results through messaging channels users already inhabit—WhatsApp, Telegram, Slack, Discord, iMessage, and many others—so the Agent can be reached from almost anywhere. Its local-first Gateway, together with authorized tools, plugins, and Skills, can connect cloud applications such as Google Drive and Notion as well as the local file system. Files scattered across accounts and devices can therefore, with the user's explicit authorization, enter one Agent's observation space and be acted on by its tools. Compared with the original cloud-sandbox-centered form of Manus, where files generally had to be uploaded or a connector separately configured, local-first OpenClaw spans a broader data boundary. Manus later added its own Google Drive connector and desktop access to local files—which only reinforces the point: product evolution often consists precisely of expanding observation and action spaces[^ch1-agent-products].\n\nExpansion does not mean dumping every available token and tool into the model at once. Irrelevant context adds noise, while too many tools increase selection cost and security risk. Useful expansion must be **on-demand, relevant, and controlled**: retrieval should place the right information in context, tool discovery should expose only the actions currently needed, and permissions and result verification should constrain those actions. Later chapters develop each of these techniques.\n\n[^ch1-agent-interface]: John L. Hennessy and David A. Patterson, *Computer Architecture: A Quantitative Approach*, 6th ed., Morgan Kaufmann, 2019, Chapter 1, “What Is Computer Architecture?” The book distinguishes instruction set architecture, computer organization, and hardware; the ISA is specifically the interface between software and hardware. See https://shop.elsevier.com/books/computer-architecture/hennessy/978-0-12-811905-1\n\n[^ch1-agent-products]: Manus's official materials describe its original Sandbox as an isolated cloud virtual machine. When introducing its Google Drive Connector, Manus explicitly recalled the earlier, fragmented workflow of manually downloading and uploading files between Drive, the desktop, and Manus. When it launched My Computer in March 2026, it called the fact that important work lives locally rather than in the cloud a fundamental limitation of the cloud sandbox. OpenClaw's official README describes a local-first, always-on personal assistant running on the user's own devices and lists more than twenty messaging channels; its tools and plugin system can add cloud integrations and local capabilities. See https://manus.im/blog/manus-sandbox, https://manus.im/blog/manus-google-drive-connector, https://manus.im/blog/manus-my-computer-desktop, https://github.com/openclaw/openclaw, and https://docs.openclaw.ai/tools\n\nUnderstanding what each component does, and how they fit together, is the foundation for building effective Agent systems. We will begin with the most concrete of the three—tools, the action interfaces—and work inward to the LLM and context. First, here is how different kinds of Agents compare across these three dimensions:\n\n\n\nANONYMOUS CHINESE X:\n### 与人工智能代理入门 [第1/9部分]\n# 人工智能代理入门\n\n如果你使用过Cursor编写代码，并且看到它搜索你的代码库、编辑多个文件并重新运行测试直到通过，那么你已经使用过人工智能代理了。如果你使用过Deep Research通过反复搜索和阅读来研究某个主题、让Manus控制浏览器完成在线任务、让豆包手机助手订票或发送消息，或者让Pine AI协商更低的电信账单，也是如此。\n\n这些产品有多种形式，但它们有一个共同特征：它们不再是被动的“你问，它回答”的对话。它们会规划自己的执行步骤，调用每个任务所需的工具，并根据结果调整策略。人工智能代理正在成为与计算机交互的一种新方式。\n\n本章从实际示例开始，逐步深入到人工智能代理的核心组件：读者将亲身体验现代代理能做什么，了解其背后的架构，并学习构建代理系统的设计模式和最佳实践。\n\n> **阅读提示**：本章是整本书的概念图：对核心公式、操作循环、工程框架和代理设计模式进行简洁概述。它建立了贯穿后续章节的共享词汇和参考点。第一次阅读时不要试图记住每个概念；要把握大局。后面的每个章节都会扩展这里介绍的一个方面，你可以在需要重新定位时回到本章。\n\n## 现代代理 = 大语言模型 + 上下文 + 工具\n\n现代代理系统的本质可以用一个简洁的公式概括：**代理 = 大语言模型（LLM） + 上下文 + 工具**。这个公式简单实用——只要对每个术语进行宽泛理解：\n\n- **大语言模型是代理的推理引擎**：它不仅仅是一组模型参数；它是代理的决策核心，负责理解意图、推理、规划和判断。大语言模型的能力来自于**预训练**期间获取的世界知识和语言能力，以及通过**后训练**编码的决策策略（第7章将介绍监督微调、强化学习等技术）。\n- **上下文是代理的工作信息集**：不仅仅是输入模型的文本，而是代理在每个决策点可用的工作信息集——环境、用户记忆、领域知识、自身状态和任务进展。就像一个人做决策时需要评估情况、回忆相关经验并参考资料一样，代理的上下文窗口包含了它在那一刻可以使用的信息。\n- **工具是代理的行动接口**：不仅仅是少数可调用的API函数，而是代理可以采取行动的全套方式——从预定义的工具调用到来按需加载的技能，从生成代码即时创建新能力到将工作委托给子代理，从与用户互动到响应外部事件。\n\n更直观地说：**代理 = 推理引擎 + 工作上下文 + 行动接口**。模型进行推理和决策，上下文提供这些决策所依赖的工作信息集，工具提供决策影响外部世界的接口。\n\n这三个组件正好对应强化学习（RL）中的三个核心概念（第7章有介绍）。以下表格是**可选阅读**——如果你没有强化学习背景，可以随意跳过；后面的内容不依赖它。它仅帮助熟悉强化学习的读者将相关知识映射到本书的术语中：\n\n| 直觉           | 代理组件 | RL概念（可选） | 角色                                                         |\n|----------------|----------|----------------|--------------------------------------------------------------|\n| **推理引擎**   | 大语言模型 | **策略**       | 决定“下一步做什么”的决策逻辑——根据当前信息，从所有可用选项中选择最合适的行动 |\n| **工作上下文** | 上下文   | **观测空间**   | 代理可用的所有信息——它能观察、读取、记住的内容以及能访问的系统 |\n| **行动接口**   | 工具     | **行动空间**   | 代理能做的全套事情——从发送消息到执行代码再到控制接口的“手段” |\n\n### 观测空间和行动空间：模型与世界的接口\n\n在他们的经典教科书《计算机体系结构：一种定量方法》中，亨尼西和帕特森在第1章以“什么是计算机体系结构？”开篇，并将**指令集体系结构**（ISA）确定为软件和硬件之间的接口[^ch1-agent-interface]。这种视角为我们理解代理提供了一种有用的方式：**观测空间和行动空间共同构成了大语言模型与其外部环境之间的接口**。观测空间将环境中的信息转化为模型可以处理的上下文；行动空间将模型决策转化为对外部世界的操作。观测空间之外的信息对模型来说实际上不存在。行动空间之外的操作仍然是模型只能用语言推荐的事情，即使它完全知道应该做什么。\n\n因此，**一旦底层模型保持不变，提高代理性能的主要系统工程手段通常是重新定义或扩展其观测空间和行动空间**。用本书的术语来说，这意味着扩展上下文和工具。许多看似需要“更智能模型”的问题实际上是接口问题：将与任务相关的数据带入上下文，或者将所需操作暴露为工具，那么之前无法解决的任务可能在不重新训练模型的情况下变得可解决。\n\n**Manus：合并原本独立的空间**。在Manus出现之前，生产型代理主要遵循三条不同的路线：深度研究、编码和计算机使用。Manus是第一个在一个系统中广泛有影响力地将这三者整合在一起的生产型代理。网络扩大了它的观测空间；文件系统和代码执行扩大了它的行动空间；屏幕感知以及点击和输入将图形界面带入了两者。Manus不仅仅通过替换更强的模型成为通用代理。它整合了三种代理的观测空间和行动空间，使一个代理能够跨越之前的产品边界。\n\n**OpenClaw：将接口扩展到用户的数字生活**。OpenClaw再次将两个空间向外扩展。它通过用户已经身处的消息通道——WhatsApp、Telegram、Slack、Discord、iMessage等——接收任务并返回结果，因此几乎可以从任何地方接触到代理。它的本地优先网关，加上授权的工具、插件和技能，可以连接谷歌云端硬盘和Notion等云应用以及本地文件系统。因此，在用户明确授权的情况下，分散在各个账户和设备上的文件可以进入一个代理的观测空间，并由其工具进行操作。与最初以云沙盒为中心的Manus形式相比，本地优先的OpenClaw跨越了更广泛的数据边界。Manus后来添加了自己的谷歌云端硬盘连接器和对本地文件的桌面访问——这进一步强化了这一点：产品演进通常正是通过扩展观测空间和行动空间来实现的[^ch1-agent-products]。\n\n扩展并不意味着立即将所有可用的词元和工具都注入模型。不相关的上下文会增加噪声，而工具太多会增加选择成本和安全风险。有用的扩展必须是**按需、相关且受控的**：检索应该将正确的信息放入上下文，工具发现应该只暴露当前需要的行动，权限和结果验证应该约束这些行动。后面的章节将详细介绍这些技术。\n\n[^ch1-agent-interface]: 约翰·L·亨尼西和大卫·A·帕特森，《计算机体系结构：一种定量方法》，第6版，摩根·考夫曼出版社，2019年，第1章“什么是计算机体系结构？”。该书区分了指令集体系结构、计算机组织和硬件；指令集体系结构专门是软件和硬件之间的接口。见https://shop.elsevier.com/books/computer-architecture/hennessy/978-0-12-811905-1\n\n[^ch1-agent-products]: Manus的官方资料描述其原始沙盒是一个孤立的云虚拟机。在介绍其谷歌云端硬盘连接器时，Manus明确回忆了早期在云端硬盘、桌面和Manus之间手动下载和上传文件的分散工作流程。当它在2026年3月推出“我的电脑”时，它将重要工作主要存在本地而不是云端称为云沙盒的一个基本限制。OpenClaw的官方README描述了一个在用户自己设备上运行的本地优先、始终在线的个人助手，并列出了二十多个消息通道；其工具和插件系统可以添加云集成和本地功能。见https://manus.im/blog/manus-sandbox，https://manus.im/blog/manus-google-drive-connector，https://manus.im/blog/manus-my-computer-desktop，https://github.com/openclaw/openclaw，以及https://docs.openclaw.ai/tools\n\n理解每个组件的作用以及它们如何协同工作，是构建有效代理系统的基础。我们将从三个组件中最具体的一个——工具，即行动接口——开始，向内深入到大语言模型和上下文。首先，以下是不同类型的代理在这三个维度上的比较：\n\nANONYMOUS CHINESE Y:\n### 人工智能代理入门[第1/9部分]\n### 人工智能代理入门\n\n如果你使用过Cursor编写代码，并且看到它搜索你的代码库、编辑多个文件并重新运行测试直到通过，那么你已经使用过人工智能代理了。如果你使用过Deep Research通过反复搜索和阅读来研究一个主题、让Manus控制浏览器完成在线任务、让豆包手机助手订票或发送消息，或者让Pine AI协商更低的电信账单，情况也是如此。\n\n这些产品有多种形式，但它们有一个共同特征：它们不再是被动的“你问，它回答”的对话。它们规划自己的执行步骤，调用每个任务所需的工具，并根据结果调整策略。人工智能代理正在成为与计算机交互的一种新方式。\n\n本章从实际示例开始，逐步回溯到人工智能代理的核心组件：读者将亲身体验现代代理能做什么，了解其背后的架构，并学习构建代理系统的设计模式和最佳实践。\n\n> **阅读提示**：本章是整本书的概念图：对核心公式、操作循环、工程框架和代理设计模式的简洁导览。它建立了贯穿后续章节的通用词汇和参考点。第一次阅读时不要试图记住每个概念；着眼于大局。后面的每一章都会扩展这里介绍的一个方面，你可以在需要重新定位时回到本章。\n\n### 现代代理 = 大语言模型 + 上下文 + 工具\n\n现代代理系统的本质可以用一个简洁的公式概括：**代理 = 大语言模型（LLM） + 上下文 + 工具**。这个公式简单实用——前提是每个术语都要宽泛理解：\n\n- **大语言模型是代理的推理引擎**：它不仅仅是一组模型参数；它是代理的决策核心，负责理解意图、推理、规划和判断。大语言模型的能力来自于预训练期间获取的世界知识和语言能力，以及通过后训练编码的决策策略（第7章将介绍监督微调、强化学习等技术）。\n- **上下文是代理的工作信息集**：不仅仅是输入模型的文本，而是代理在每个决策点可用的工作信息集——环境、用户记忆、领域知识、自身状态和任务进度。就像一个人做决策时需要评估情况、回忆相关经验并参考资料一样，代理的上下文窗口包含了它在那一刻可以使用的信息。\n- **工具是代理的行动接口**：不仅仅是少数可调用的API函数，而是代理可以采取行动的全套方式——从预定义的工具调用到按需加载的技能，从生成代码即时创建新能力到将工作委托给子代理，从与用户互动到响应外部事件。\n\n更直观地说：**代理 = 推理引擎 + 工作上下文 + 行动接口**。模型进行推理和决策，上下文提供这些决策所依赖的工作信息集，工具提供决策影响外部世界的接口。\n\n这三个组件正好对应强化学习（见第7章）中的三个核心概念。下表是**可选阅读**——如果你没有强化学习背景，可以随意跳过；后面的内容不依赖它。它仅用于帮助懂强化学习的读者将相关知识映射到本书的术语中：\n\n| 直觉         | 代理组件 | 强化学习概念（可选） | 角色                                                         |\n|--------------|----------|----------------------|--------------------------------------------------------------|\n| **推理引擎** | 大语言模型 | **策略**             | 决定“下一步做什么”的决策逻辑——根据当前信息，从所有可用选项中选择最合适的行动 |\n| **工作上下文** | 上下文   | **观测空间**         | 代理可用的所有信息——它能观察、读取、记住的内容以及它能访问的系统 |\n| **行动接口** | 工具     | **行动空间**         | 代理能做的全套事情——可用的“手段”，从发送消息到执行代码到控制接口 |\n\n### 观测空间和行动空间：模型与世界的接口\n\n在他们的经典教科书《计算机体系结构：一种定量方法》中，亨尼西和帕特森在第1章以“什么是计算机体系结构？”开篇，并将**指令集体系结构**（ISA）确定为软件和硬件之间的接口[^ch1-agent-interface]。这种视角为我们理解代理提供了一种有用的方式：**观测空间和行动空间共同构成了大语言模型与其外部环境之间的接口**。观测空间将环境中的信息转换为模型可以处理的上下文；行动空间将模型的决策转换为对外部世界的操作。观测空间之外的信息对模型来说实际上不存在。行动空间之外的操作仍然是模型只能用语言推荐的事情，即使它完全知道应该做什么。\n\n因此，**一旦底层模型保持不变，提高代理性能的主要系统工程手段通常是重新定义或扩展其观测空间和行动空间**。用本书的术语来说，这意味着扩展上下文和工具。许多看似需要“更智能模型”的问题实际上是接口问题：将与任务相关的数据带入上下文，或者将所需操作暴露为工具，之前无法解决的任务可能在不重新训练模型的情况下变得可解决。\n\n**Manus：合并原本独立的空间**。在Manus出现之前，生产型代理主要遵循三条不同的路径：Deep Research、Coding和Computer Use。Manus是第一个在一个系统中将这三者整合在一起的具有广泛影响力的生产型代理。网络扩大了它的观测空间；文件系统和代码执行扩大了它的行动空间；屏幕感知以及点击和输入将图形界面带入了两者。Manus不仅仅通过替换更强的模型成为通用代理。它将三种代理的观测空间和行动空间结合在一起，使一个代理能够跨越之前的产品边界。\n\n**OpenClaw：将接口扩展到用户的数字生活**。OpenClaw再次将两个空间向外扩展。它通过用户已经使用的消息通道——WhatsApp、Telegram、Slack、Discord、iMessage等接收任务并返回结果，因此几乎可以从任何地方接触到该代理。它的本地优先网关以及授权的工具、插件和技能可以连接谷歌云端硬盘和Notion等云应用以及本地文件系统。因此，在用户明确授权的情况下，分散在不同账户和设备上的文件可以进入一个代理的观测空间，并由其工具进行操作。与最初以云沙盒为中心的Manus形式相比，在Manus中文件通常必须上传或单独配置连接器，而本地优先的OpenClaw跨越了更广泛的数据边界。Manus后来添加了自己的谷歌云端硬盘连接器和对本地文件的桌面访问——这只会强化这一点：产品演进通常恰好包括扩展观测空间和行动空间[^ch1-agent-products]。\n\n扩展并不意味着立即将每个可用的标记和工具都放入模型中。不相关的上下文会增加噪声，而太多工具会增加选择成本和安全风险。有用的扩展必须是**按需、相关且受控的**：检索应该将正确的信息放入上下文中，工具发现应该仅暴露当前需要的行动，权限和结果验证应该约束这些行动。后面的章节将发展这些技术。\n\n[^ch1-agent-interface]: 约翰·L·亨尼西和大卫·A·帕特森，《计算机体系结构：一种定量方法》，第6版，摩根·考夫曼出版社，2019年，第1章“什么是计算机体系结构？”。该书区分了指令集体系结构、计算机组织和硬件；指令集体系结构专门是软件和硬件之间的接口。见https://shop.elsevier.com/books/computer-architecture/hennessy/978-0-12-811905-1\n\n[^ch1-agent-products]: Manus的官方材料描述其原始沙盒是一个孤立的云虚拟机。在介绍其谷歌云端硬盘连接器时，Manus明确回忆了早期在云端硬盘、桌面和Manus之间手动下载和上传文件的分散工作流程。当它在2026年3月推出My Computer时，它称重要工作本地存在而不是在云端是云沙盒的一个基本限制。OpenClaw的官方README描述了一个在用户自己设备上运行的本地优先、始终在线的个人助手，并列出了二十多个消息通道；其工具和插件系统可以添加云集成和本地功能。见https://manus.im/blog/manus-sandbox，https://manus.im/blog/manus-google-drive-connector，https://manus.im/blog/manus-my-computer-desktop，https://github.com/openclaw/openclaw，以及https://docs.openclaw.ai/tools\n\n理解每个组件的作用以及它们如何组合在一起，是构建有效代理系统的基础。我们将从三者中最具体的一个——工具，即行动接口开始，向内深入到大型语言模型和上下文。首先，以下是不同类型的代理在这三个维度上的比较："
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