Core Insights - The rapid development of AI Agents has ignited a trend of "everything can be an Agent," particularly evident in the competitive landscape of model development and application [1][2][10] - Major companies like OpenAI, Google, and Alibaba are heavily investing in the Agent space, with new products emerging that enhance user interaction and decision-making capabilities [2][7][8] - The evolution of AI applications is categorized into three phases: prompt-based interactions, workflow-based systems, and the current phase of AI Agents, which emphasize autonomous decision-making and tool usage [17][19] Group 1: Model Development - The AI sector has entered a "arms race" for model development, with significant advancements marked by the release of models like DeepSeek, o3 Pro, and Gemini 2.5 Pro [5][6][14] - The introduction of DeepSeek has demonstrated that there is no significant gap between domestic and international model technologies, prompting major players to accelerate their model strategies [6][10] - The focus has shifted from "pre-training" to "post-training" methods, utilizing reinforcement learning to enhance model performance even with limited labeled data [11][13] Group 2: Application Development - The launch of OpenAI's Operator and Deep Research has marked 2025 as the "Year of AI Agents," with a surge in applications that leverage these capabilities [7][8] - Companies are exploring various applications of AI Agents, with notable examples including Cursor and Windsurf, which have validated product-market fit in the programming domain [9][21] - The ability of Agents to use tools effectively has been a significant breakthrough, allowing for enhanced information retrieval and interaction with external systems [20][21] Group 3: Challenges and Opportunities - Despite advancements, AI Agents face challenges such as context management, memory mechanisms, and interaction with complex software systems [39][40] - The future of Agent applications may involve evolving business models, potentially shifting from subscription-based to usage-based or outcome-based payment structures [40][41] - The industry is witnessing a competitive landscape where vertical-specific Agents may offer more value due to their specialized knowledge and closer user relationships [42][46]

编译：haozhen, Cookie AI agent 并不是一个新概念，但从 2024 年到今天，agent 的行动能力和交互方式发生了质变，头部模型厂商也正在将 agentic 能力融入模型，agentic 能 力会成为今年模型竞赛的重点之一， tool use 作为 agent 最重要的能力，一直是头部 AI labs 非常关注的方向。上周，OpenAI 发布了新一代模型 o3， o3 有最丰富的 tool use 方式。 本文是对 OpenAI agent 团队访谈的编译，OpenAI agent 产品和工程负责人分享了 OpenAI 在 agent 开发与工具生态方面的技术细节，以及他们对开发 者实践的观察与见解。他们认为，受益于 CoT 与 tool use 的结合，agent 获取信息的方式已经发生了巨变，agent 的下一步是能够接入数百个工具，并 能够自主判断调用哪个工具并确定如何使用。此外，multi agent 系统的工作效率会更高，且具有更高的可控性和优化潜力。 我们判断， multi agent 系统会在今年有大的突破，vertical agent 会因此直接受益，在 compute ...

以下文章来源于海外独角兽 ，作者拾象 Founder Park 正在搭建开发者社群，邀请积极尝试、测试新模型、新技术的开发者、创业者们加入，请扫码详细填写你的产品/项目信息，通过 审核后工作人员会拉你入群～ 海外独角兽 . 研究科技大航海时代的伟大公司。 Deep Research 产品可被理解为 一个以大模型能力为基础、集合了检索与报告生成的端到端系统，对信息进行迭代搜索和分析，并生成详细报告作为输 出。 参考 Han Lee 的 2x2 分析框架，目前 Deep Research 类产品在 输出深度、训练程度 两大维度呈现分异。 输出深度 即产品在先前研究成果的基础上进行了 多少次迭代循环以收集更多信息，可进一步被理解为 Agentic 能力的必要基础。 低训练程度 指代经过人工干预和调整的系统，比如使用人工调整的 prompt，高训练程度则是指利用机器学习对系统进行训练。 和传统 LLM Search 产品相比，Deep Research 是迈向 Agent 产品雏形的一次跃迁，可能也将成为具有阶段代表性的经典产品形态。 Deep Research 产品通过系列推理模型嵌入，已生长出了 Agent 产品 ...