Core Viewpoint - The year 2025 is anticipated to be a breakthrough year for Agents, significantly enhancing the capabilities of large models and transforming human-computer interaction across various platforms, particularly in multi-task automation [1]. Group 1: Agent Development and Platforms - The emergence of the first general-purpose Agent product, Manus, has garnered unprecedented attention, with major internet companies and startups focusing on Agents as a key area of AI competition [2]. - At the recent Force 2025 conference, Agents were highlighted alongside the latest version of the Doubao large model series [3]. - The conference's main forum showcased a new paradigm for AI cloud-native Agent development, emphasizing how Agents can reshape productivity [4]. - The Doubao platform has evolved into a "full lifecycle platform," addressing diverse development and tuning needs for Agents in the era of large models [5]. Group 2: Doubao Platform Features - The Doubao development platform enables low-code Agent development, allowing users with no coding experience to quickly build and deploy Agents across various channels [8]. - The platform empowers Agent development through four main aspects: intelligent IDE, application IDE, a rich set of plugins and workflow templates, and enterprise-level security capabilities [9]. - The application IDE, set to launch in 2024, will allow developers to create GUI-based applications using drag-and-drop features [10]. - Pre-configured Agent templates facilitate rapid deployment of functional Agents, such as smart customer service assistants and educational assistants [12]. Group 3: Eino Framework - Eino, a Go language-based LLM application development framework, draws inspiration from open-source communities and emphasizes simplicity, scalability, reliability, and effectiveness [13]. - Eino standardizes core modules for Agent development, enabling seamless integration with both open-source and closed-source models [14]. - The framework supports flexible orchestration capabilities for complex task decomposition and multi-tool collaboration [15]. - Over 300 systems have been developed internally at ByteDance using Eino, with a GitHub star count of 4.3k, indicating growing interest among developers [16]. Group 4: Agent Lifecycle Management - The Doubao platform establishes a comprehensive Agent lifecycle system encompassing development, evaluation, online observation, and optimization [16]. - The evaluation phase includes quantifying Agent performance to ensure it meets standards, while the observation phase involves real-time data collection and analysis [19]. - Developers can analyze user queries and behavior to adjust Agent performance, identifying and addressing issues through a robust observation system [20]. - The platform supports flexible evaluation set management, allowing developers to create and manage evaluation sets easily [22]. Group 5: Doubao Space - Doubao Space, launched in April, serves as a collaborative platform for high-quality Agents, facilitating efficient task resolution through expert collaboration [25]. - Users can leverage Doubao Space for market analysis, academic guidance, and expert support, with capabilities continuously expanded through the MCP protocol [26]. - The Doubao platform is expected to become foundational infrastructure for Agent development in the era of large models [27].

大厂团队开发 AI 产品的样本。 4 月 18 日周五晚，字节的 Agent 产品 "扣子空间（space.coze.cn） " 开启内测。团队为此准备不少算 力资源，但短短几小时内，服务器就被涌入的用户挤爆。 超出预期的用户热情，让扣子团队再次验证一个判断：用户一直在等待能用的 AI 产品，去解决工作 中的问题。 ChatGPT 让聊天窗成为大模型应用首选的交互界面。理由是当 AI 足够聪明，似乎就不需要用户学 习，不用熟悉按钮和菜单、只用自然语言下命令就够了。 字节在 2023 年下半年搭起 AI 应用开发平台 "扣子（Coze）"，让开发者不需要熟悉复杂的技术能 力，就能将自己的数据接入最前沿的大模型，做各式各样的应用。到 2024 年中，扣子团队发现，尽 管聊天机器人应用成千上万地出现，从知识问答到情感陪伴，覆盖几乎所有热门的场景，但大多面临 相同的问题：用户增长难，留存更难。 这是一个产品形态与用户真实需求形成偏差的案例。聊天界面简单易用，但对大模型要求极高，导致 不论中美都是一两个通用 AI 聊天应用断层式领先。扣子团队发现，平台上有一类应用的增长和留存 明显更好——嵌入到用户工作流的大模型应用， ...

ChatGPT 让聊天窗成为大模型应用首选的交互界面。理由是当 AI 足够聪明，似乎就不需要用户学 习，不用熟悉按钮和菜单、只用自然语言下命令就够了。 字节在 2023 年下半年搭起 AI 应用开发平台 "扣子（Coze）"，让开发者不需要熟悉复杂的技术能 力，就能将自己的数据接入最前沿的大模型，做各式各样的应用。到 2024 年中，扣子团队发现，尽 管聊天机器人应用成千上万地出现，从知识问答到情感陪伴，覆盖几乎所有热门的场景，但大多面临 相同的问题：用户增长难，留存更难。 这是一个产品形态与用户真实需求形成偏差的案例。聊天界面简单易用，但对大模型要求极高，导致 不论中美都是一两个通用 AI 聊天应用断层式领先。扣子团队发现，平台上有一类应用的增长和留存 明显更好——嵌入到用户工作流的大模型应用，针对具体工作场景解决具体问题。 这成为他们去年下半年的工作重点。用大模型改造工作流，在工作场景中完成繁琐的任务，正是当下 行业热议的 Agent 初始形态。 大厂团队开发 AI 产品的样本。 4 月 18 日周五晚，字节的 Agent 产品 "扣子空间（space.coze.cn） " 开启内测。团队为此准备不少算 ...