作者：Lai、bryan、haozhen 编辑：penny 我们之前已经研究了 Browserbase 、 E2B 等公司，本文是我们对于 Agent Infra 领域图景更全面的 Mapping。我们划分出了 Environment、Context、Tools、Agent Security 这四大赛道，逐步分析每个环 节的价值和值得关注的初创公司： • Environment 的作用是给 Agent 执行任务提供容器，是一个 Agent-native computer； • Context 层是在 Agent 工作中赋予记忆 Memory 和领域知识的重要中间层； • Tools 由于 MCP 协议的统一而百花齐放，同时目前 Tools 的核心用户还是开发者，普通用户的使用 门槛太高； 2025 年以来，Agent 开发量和使用量都有明显提高。Agent 的爆发带来了 Agent Infra 需求的爆发。在 过去 1-2 年，Agent 开发大多依赖开发者手动使用传统 Infra 搭建，开发工程量大、流程复杂，但随 着越来越多 Agent-native Infra 涌现，Agent 开发的难度和周期都在缩小 ...

Core Insights - The article emphasizes the resilience and growth potential of AGIX in the AI sector, highlighting its recent performance and the importance of companies effectively utilizing AI to drive revenue growth [1][4]. Group 1: AGIX Growth Review - AGIX has shown a significant increase of 23.15% over the past month, outperforming Nasdaq100, which grew by 11.76% [6]. - Among the 45 companies covered by AGIX, 36 companies (78%) exceeded the growth of Nasdaq100, with 14 companies achieving over 30% growth [6]. - The article notes that AGIX's maximum drawdown was -31.48%, which is within the typical volatility range for AI-related assets [1][19]. Group 2: AGIX as a Collection of High-Growth Stocks - The article identifies AGIX as a collection of high-growth stocks in the AI era, with a focus on mid-cap companies rather than just the largest tech firms [16]. - Companies like Duolingo and Palantir have demonstrated high volatility and growth potential, with Duolingo's stock doubling from its lowest point in two months [18][36]. - The article suggests that the high volatility of AGIX is a common characteristic of high-growth sectors, where short-term fluctuations are expected in pursuit of long-term growth [19][24]. Group 3: 1Q2025 Earnings Season: Dispel of AI Skepticism - The earnings season has shown that AI is creating tangible value, with companies like Applovin reporting significant revenue growth attributed to AI optimizations [34]. - Duolingo's AI-driven features have led to a 38% year-over-year revenue increase, showcasing the practical application of AI in enhancing user engagement [36]. - ServiceNow's focus on AI for business transformation highlights the growing demand for AI solutions to improve efficiency and reduce costs in various industries [46].

Group 1 - E2B is an emerging player in the multi-agent system space, providing a secure sandbox environment for running AI-generated code, with a significant increase in sandbox creation from 40,000 to 15 million in one year, a growth of 375 times [7][10][11] - The founders of E2B, Vasek Mlejnsky and Tomas Valenta, previously worked on a product called DevBook, which laid the groundwork for E2B's sandbox technology [7][9] - E2B aims to become the AWS of the AI agent era, providing an automated infrastructure platform that will support GPU capabilities for complex data analysis and application hosting [4][13] Group 2 - E2B's vision includes evolving from a code interpreter to a more general agent runtime environment, recognizing the importance of a secure and flexible code execution environment for AI agents [9][10] - The platform supports multiple programming languages, with Python and JavaScript being the most used, indicating a strong developer interest [11] - E2B is observing a trend where code execution is not only for developers but also for non-developer users, expanding its potential market [22][23] Group 3 - E2B is strategically located in Silicon Valley to be closer to its target user base of AI application developers, facilitating direct support and faster product development [62][64] - The company recognizes the challenges of pricing infrastructure services, emphasizing the need for clear pricing logic and user control over expenses [30] - E2B is exploring the potential of computer use agents, which could automate tasks on personal computers, presenting both opportunities and challenges in user control and security [31][32][35]

Core Viewpoint - OpenEvidence is positioned as a leading AI diagnostic tool in the medical field, addressing the challenges of information overload and the rapid growth of medical knowledge, thereby enhancing diagnostic efficiency and decision-making quality for physicians [4][10][11]. Group 1: Background - The medical field faces unprecedented challenges due to the explosive growth of medical knowledge, with literature increasing at a rate of one article every two minutes, leading to significant information overload for doctors [9][10]. - The World Health Organization reports that doctors in low-income countries access cutting-edge medical evidence only 1/9 as frequently as those in high-income countries, highlighting a significant "cognitive gap" [10]. - The aging population and the prevalence of complex cases further complicate clinical decision-making, with traditional guidelines covering less than 7% of scenarios involving polypharmacy [10][11]. Group 2: Product and Technology - OpenEvidence is a chatbot designed to assist medical professionals by providing efficient and accurate clinical support, featuring a unique interface that ensures traceability and verification of information [12][13]. - The product offers dual modes of response: "care guidelines" and "clinical evidence," catering to practical advice and theoretical data support [12]. - OpenEvidence has demonstrated high reliability, scoring over 90% on the USMLE, significantly outperforming general AI models like ChatGPT [16][19]. Group 3: Commercialization and Competition - OpenEvidence employs a direct-to-user growth strategy, bypassing traditional procurement processes in healthcare, which often take years [21][22]. - The company has achieved rapid growth, reaching approximately 100,000 monthly users within a year, covering 10%-25% of practicing physicians in the U.S. [22][23]. - OpenEvidence's business model focuses on targeted advertising, integrating ads from pharmaceutical and medical device companies into the clinical decision-making process [25][26]. Group 4: Team and Financing - The founder, Daniel Nadler, has a strong academic background in economics and AI, with previous successful ventures in the AI space [30][34]. - OpenEvidence secured $75 million in Series A funding from Sequoia Capital in February 2025, achieving a post-money valuation exceeding $1 billion [36].

Healthcare 是美国最大的行业之一，支出占 GDP 的 17%，雇佣 1/10 的美国劳动力。它也极其低效，美国每年 4.5 万亿美元的医疗支出中，有高达 25%，也 就是 1.1 万亿美元被视为无效或可避免的浪费。在某些情况下，healthcare 从业者用于保险账单处理的时间成本可能占账单收入的 1/7。碎片化的系统、低 效的运营流程和人力密集的环节，是 AI Agent 的天然切入点。如果 AI 能切掉哪怕一小部分，就可能创造几千亿级别的新市场空间。 过去一年，GenAI 在医疗行业的渗透加速了。本文我们系统梳理了 Healthcare AI 的产业结构，重点聚焦了在哪些环节 AI 能够真正创造价值、并且拥有明 确预算，识别了值得优先关注和投资的细分方向和公司。 医疗行业的独特性决定了 AI 的扩散路径和传统软件或消费级应用并不同。目前 Healthcare AI 的高价值切入口，集中在 高频刚需、非临床环节 ，一方面是 前台任务 ，如提升医生效率的 Patient Copilot ( Case Study 包括 Abridge、Ambience、OpenEvidence )；另一方面是 后台基 ...

作者：cage, haozhen 我们在 2025 年 Q1 的大模型季报 中提到，在 AGI 路线图上，只有智能提升是唯一主线，因此我们持 续关注头部 AI Lab 的模型发布。上周 OpenAI 密集发布了 o 系列最新的两个模型 o3 和 o4-mini，开 源了 Codex CLI，还推出了在 API 中使用的 GPT 4.1。本文将着重对这些新发布进行解读，尤其是 o3 agentic 和多模态 CoT 新能力。 我们认为 OpenAI 在数次平淡的更新后，终于拿出了有惊艳表现的 o3。融合了 tool use 能力后，模型 表现已经覆盖了 agent 产品常用的 use case。Agent 产品开始分化出两类路线：一类是像 o3 那样把 和 o3 的发布模式一样， OpenAI 的 reasoning model 都是先训练出一个 mini reasoning 版本，再 scale 到 一个 long inference time、full tool use 能力的模型上。 而之前 GPT 模型总是先训练出最大的模型，再蒸 馏到小模型上。这个策略值得探讨其原因，我们的猜测是 RL 算法比较脆弱， ...

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 ...

作者：张昊然，Motiff 妙多 Co-Founder、副总裁 编辑：Cage 曾被专业设计师看成"玩具"的生成式 UI，如今正在和 vibe coding 一起改写开发和设计工作流，需求- >代码->设计的新工作流开始出现。本文回溯了这场演变：从早期「拼乐高」式的模板化设计，到 Claude Sonnet 3.5 更新开始模型有了创造力、直接写出高美感和风格化的前端代码，到如今 AI 展现 出理解并遵循特定"设计系统"的能力。 AI 设计的表达力和风格多样性这两个维度上实现了跃迁式进步，让我们开始期待未来有 AI-native 的 设计编辑器，设计中的 70%+ 工作由 AI 完成，类似设计领域的 Cursor 甚至 Devin。设计师的价值不 再是操作设计工具进行构建，而是回归设计本身进行更多的思考、呈现更多的创意方案、推进更高 质量的决策。 本文是一篇读者投稿，来自 Motiff 妙多的 Cofounder 昊然。他基于这两年打造 AI-native 设计工具的 经验，对 AI+设计领域的未来可能性进行了推演，尤其是如何在模型能力的飞速进展下对业务方向 和技术路线作出决策。相信来自优秀读者朋友的实践和观 ...

作者：Krystal 编辑：penny 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。 从 2024 年末问世的 Google Deep Research，到 2024 年 2 月以来密集发布的 OpenAI Deep Research、 Perplexity、xAI Deep Search、Manus，Deep Research 成为各家 Agent 产品角逐的白热化赛道。 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。 ...

编译：linlin, haina 海外独角兽原创编译 转载请注明 2. Customer Support是世界最大 SaaS 市场之一： Salesforce 从 Service Cloud （即工单系统）获得的 83 亿美元收入甚至超过了从 Sales Cloud 或 CRM 获得 的 75 亿美元收入。世界上最大的 SaaS 公司从其支持系统获得的收入最多，，这正是Pylon决定进入的领域。 3. B2B 沟通方式正在全渠道化： 增长最快的公司都是那些抓住新兴趋势并随之成长的公司。 对 Pylon 来说，这一趋势是 B2B 企业与客户沟通正在变得更 加 Omnichannel。 AI Customer Support 是我们持续关注的领域。客户关系管理直接影响 B2B 客户留存与拓展。然而现有工具多针对 B2C 场景，B2B 支持渠道尚不完 善。基于 B2B 复杂的 Customer Support 链条与产品需求，初创公司 Pylon 正在打造专为 B2B 企业全栈 Customer Support 团队设计的协同工作平台。 Pylon 也是我们从硅谷 founder 口中经常听到的名字。 Pylon ...