都2026了，你还在为写提示词掉头发吗？ 我知道，大伙儿不管上班的上学的早就离不开 AI 了，但我的评价是，最折磨人的，还得是用 AI 的前戏，因为 AI 是很难一句话，就听懂你想要什么的。 得把一大坨一大坨的提示词搬来搬去，把背景需求格式交代一遍，还得把各种陈年老文件喂给AI，看着他学。 结果还没开始，AI 就顶不住了，聊天框又得remake了。 然而最近，世超发现个新项目，只能说巨火，各大 AI 社区都在讨论这玩意，甚至有说法是比提示词用着爽。 这次，他们又搓了个 Skills 回来了，看架势好像又想引领新潮流了。。别的不说，隔壁 OpenAI 反正好像已经开始抄作业了。 这玩意叫 Agent Skills ，是由 Claude 母公司 Anthropic 搞出来的。 经常看咱文章的差友可能有印象，这公司之前搞了个 MCP 协议，直接把 AI 抄家伙干活的难度打了下来，后来谷歌和 OpenAI 这种巨头全跟进了。 这玩意的作用，顾名思义，AI 现在可以像宝可梦一样，学会任意新技能了。 我们就拿 Skills 的亲兄弟 Claude 来演示下。 首先你得在设置里，找到 Skills 的开关把它打开。 然后你 ...

Core Insights - The Linux Foundation has launched the Agentic AI Foundation (AAIF), marking a shift in the AI field towards collaborative autonomous agents, seen as the "Linux moment" for AI [2] - AAIF aims to serve as a neutral hosting platform for open-source projects related to AI agents, with major tech companies like Amazon, Google, and Microsoft joining as members [2] - The foundation's initial technical pillars include three core open-source projects: MCP protocol, AGENTS.md specification, and Goose framework, contributed by Anthropic, OpenAI, and Block [2][3] Group 1 - MCP (Model Context Protocol) is designed to standardize the connection between AI agents and external data sources, likened to a "USB-C interface" for AI [3] - AGENTS.md provides a Markdown-based standard for defining agent behavior in specific projects, while the Goose framework offers a structured workflow for agent development [3] - The AAIF aims to prevent monopolization of AI agent ecosystems by establishing interoperability standards and best practices [3] Group 2 - MCP has already been implemented in over 10,000 servers, with support from major products like ChatGPT and Microsoft Copilot, indicating strong industry recognition of the open protocol [4] - Despite skepticism about the collaboration being merely a "brand alliance," proponents argue that the protocol facilitates collaboration without redundant integration efforts [4] - The AAIF's funding model includes tiered membership fees, but control over project direction is maintained by a technical steering committee, ensuring that no single member can dictate the development path [5] Group 3 - The importance of shared standards is underscored by a UiPath report indicating that 65% of enterprises will initiate agent pilot programs by mid-2025, yet only 5% have seen financial returns [5] - The AAIF aims to promote compatibility among agent development frameworks, cloud service providers, and developer tools, emphasizing that the scale of AI is determined by solution construction rather than model size [6] - Challenges remain, including concerns about the maintenance of protocols and the practical utility of the Goose framework, but the focus is on creating a sustainable ecosystem rather than perfect standards [6]

Group 1 - The core idea of the news is the establishment of the AI Agent Foundation (AAIF) by OpenAI, Anthropic, and Block to promote interoperability and open standards in the AI agent ecosystem [2][3] - The foundation aims to provide neutral management and infrastructure for AI agents, facilitating their transition from experimental stages to real-world applications [3][4] - The collaboration reflects a strategic shift among Silicon Valley giants, recognizing that open standards are more beneficial for long-term interests than closed competition in the commercialization of AI agents [3][5] Group 2 - The establishment of AAIF addresses two major industry pain points: interoperability issues and the risk of monopolistic practices in the AI agent ecosystem [4][5] - The three founding companies have donated their core technologies to ensure the foundation's neutrality, including Anthropic's MCP protocol, OpenAI's AGENTS.md, and Block's Goose framework [6][7] - These contributions aim to reduce redundant labor in building connectors, enhance consistency in agent behavior across systems, and facilitate easier deployment of agent systems in a secure environment [7] Group 3 - OpenAI and Anthropic, despite being fierce competitors in the large language model space, are collaborating to ensure an open and expansive market for AI agents [8] - The strategic interest in preventing market fragmentation or monopolization is crucial for accelerating the commercialization of AI technologies [8] - The trend towards open-source solutions is being recognized as a significant advantage, with companies like OpenAI increasing their open-source efforts to attract global developers and expand their ecosystems [8][9] Group 4 - The grand vision of AAIF is to create a modular, composable, and auditable AI agent ecosystem, akin to the internet, rather than isolated applications [9] - By leveraging the donated technologies, AAIF aims to accelerate innovation and keep the doors of the AI agent ecosystem open [9]

在人工智能技术深度渗透各行业的2025年，AI大模型应用开发正经历从"单一问答"到"自主任务执行"的范式跃迁。以MCP（Model Context Protocol）协议为 核心的智能体开发模式，凭借其标准化工具调用能力与跨生态兼容性，成为企业级AI应用落地的关键基础设施。本文将从技术原理、生态构建、实战案例 三个维度，解析这一新范式的核心价值与实践路径。 一、MCP协议：AI工具调用的"万能插座" MCP协议由Anthropic于2024年11月推出，旨在解决AI模型与外部工具交互时的碎片化问题。其核心设计理念类似于USB-C接口——通过统一标准，让AI模 型能够像人类一样调用数据库、API、文件系统等外部资源。例如，某金融智能体可通过MCP协议直接连接Wind行情接口，实时获取股票价格数据；医疗智 能体则能调用医学知识图谱，生成符合诊疗规范的建议。 技术架构上，MCP采用客户端-服务器模式： 随着MCP协议的普及，AI智能体正从专业领域走向大众市场。2025年，低代码开发平台（如活字格）已集成MCP工具市场，开发者可通过拖拽方式快速构 建智能体应用。例如，某教育机构开发的"智能作业批改助手"，教师上传学生 ...

Group 1 - The core idea of the news is the establishment of the AI Agent Foundation (AAIF) by leading companies to create open-source technical standards related to AI agents, addressing the growing conflict between AI-based tools and the internet ecosystem [1][2] - The AAIF is operated under the Linux Foundation, ensuring that the development of technical standards is not controlled by individual companies, similar to the standardization efforts in cross-bank electronic payments [1] - Founding projects of the AAIF include Anthropic's MCP protocol, OpenAI's AGENTS.md design blueprint, and Block's open-source AI agent Goose, with significant participation from major tech companies like Google, Microsoft, and Amazon [2][3] Group 2 - The MCP protocol, released by Anthropic, provides a standardized way for AI models to connect to various data sources and tools, which is essential for achieving AI agent functionality [3] - Major tech companies, including Google, Microsoft, OpenAI, Alibaba, Tencent, and Baidu, have announced their support for the MCP protocol, although developers have reported issues, particularly regarding security vulnerabilities [3] - AGENTS.md is a format developed by OpenAI for instructing coding agents, while Goose is a locally run open-source AI agent developed by Block that does not require an internet connection [3]

但有趣的是，就在今年年初，MCP还曾一度占据了AI界的头版头条，几乎所有从业者都高呼"MCP让AI 连接万物"、"AI终于有了属于自己的USB接口"、"Agent时代的基础设施"。然而仅仅半年时间过去后， MCP就从圈内人眼中的"小甜甜"，光速蜕变为"牛夫人"。 那么MCP为何会被捧上神坛，又为什么光速陨落呢？其实这是因为MCP的走红本身就很违和，属于 是"期望膨胀期"的典型产物。此外需要注意的是，MCP并非出道即巅峰，它的走红过程与ChatGPT、 DeepSeek截然不同。 不久前在11月25日，AI独角兽Anthropic发文庆祝MCP协议（模型上下文协议）诞生一周年。然而如今 整个AI业界对于Anthropic此举即便不说视而不见，也算得上是漠不关心了，这个消息在社交平台的讨 论度更是趋近于零。 不难发现，MCP是一个为智能体服务的协议，它给予了智能体获得"真功夫"的机会，这也是为什么 MCP会在今年年初走红。从某种意义上来说，先有"2025年是智能体之年"这个说法，后来才有MCP登 上舞台中央，而力推MCP则是一众AI大厂的默契。 在2024年的最后一天，OpenAI首席执行官山姆·奥特曼公布了20 ...

Core Insights - The AI industry is transitioning from hype to real business applications, marking a significant shift in its economic impact by 2025 [1][2] - AI is becoming a crucial driver of economic growth, with 16 leading AI-first companies achieving an annualized total revenue of $18.5 billion by August 2025 [2] - The 2025 "State of AI Report" by Nathan Benaich connects various developments in research, industry, politics, and security, illustrating AI's evolution into a transformative production system [3][5] Group 1: Industry Developments - 2025 is defined as the "Year of Reasoning," highlighting advancements in reasoning models like OpenAI's o1-preview and DeepSeek's R1-lite-preview [8][9] - Major companies are releasing reasoning-capable models, with OpenAI and DeepMind leading the rankings, although competition is intensifying [13][20] - The report indicates that traditional benchmark tests are becoming less reliable, with practical utility emerging as the new standard for measuring AI capabilities [25][28] Group 2: Financial Performance - AI-first companies are experiencing rapid revenue growth, with median annual recurring revenue (ARR) exceeding $2 million for enterprise applications and $4 million for consumer applications [57][60] - The growth rate of top AI companies from inception to achieving $5 million ARR is 1.5 times faster than traditional SaaS companies, with newer AI firms growing at an astonishing rate of 4.5 times [60][61] - The demand for paid AI solutions is surging, with adoption rates among U.S. enterprises rising from 5% in early 2023 to 43.8% by September 2025 [65] Group 3: Competitive Landscape - OpenAI remains a benchmark in the industry, but its competitive edge is narrowing as other models like DeepSeek and Qwen close the gap in reasoning and coding capabilities [20][30] - The report notes that the open-source ecosystem is shifting, with Chinese models like Qwen gaining significant traction over Meta's offerings [29][31] - The AI agent framework is diversifying, with numerous competing frameworks emerging, each carving out niches in various applications [36][37] Group 4: Future Predictions - The report forecasts that a real-time generated video game will become the most-watched game on Twitch, and AI agents will significantly impact online sales and advertising expenditures [97][99] - It predicts that a major AI lab will resume open-sourcing its cutting-edge models to gain governmental support, and a Chinese AI lab will surpass U.S. labs in a key ranking [99]

Core Insights - The article discusses the launch of the Agent Payments Protocol (AP2) by Google, which aims to enable AI agents to conduct transactions autonomously, marking the beginning of a trillion-dollar "Agentic Commerce" era [1][7]. Group 1: Challenges of AI Agents in Transactions - AI agents face a significant barrier to autonomous transactions due to the lack of trust in the existing financial payment systems, which are built around human behavior [3][4]. - Three critical questions arise regarding trust: authorization (how merchants can verify the AI agent's legitimacy), authenticity (how to ensure the order reflects the user's true intent), and accountability (who is responsible in case of errors) [4][6]. Group 2: Evolution of AI Payment Protocols - The AP2 protocol is the final chapter in a three-part series aimed at integrating AI into the economy, following the MCP (Agent-to-Tool) and A2A (Agent-to-Agent) protocols [7][8][13]. - MCP allowed AI agents to interact with external tools, while A2A enabled communication between different agents, setting the stage for AP2 to facilitate economic transactions [11][16]. Group 3: Mechanism of AP2 - AP2 introduces a "digital evidence chain" that includes a "mandate" system, which serves as a legally binding digital contract for each transaction [17][19]. - The process involves generating an intention mandate, a shopping cart mandate, and a payment association, ensuring that every transaction is authorized, factual, and accountable [20][21][22]. Group 4: Industry Collaboration and Future Implications - AP2 is an open-source protocol with over 60 initial partners, including major players in finance, e-commerce, and technology, indicating a collaborative effort to establish trust standards in AI commerce [24][26]. - The implementation of AP2 signifies a shift in commercial interactions, moving from human-driven interfaces to backend API-level negotiations between agents [26][27].

Core Insights - The article discusses Google's new AP2 protocol, which facilitates secure cross-platform payment transactions initiated by AI agents, providing traceable records for each transaction [2][6][7]. Group 1: AP2 Protocol Overview - AP2 is an extension of the A2A and MCP protocols, aimed at enhancing the capabilities of AI agents by enabling better integration with external resources, tools, and APIs [2][4]. - The protocol addresses three main issues: authorization, authenticity, and accountability in transactions conducted by AI agents [7]. Group 2: Functionality and Mechanism - AP2 establishes trust through the use of Mandates (authorization documents), which are tamper-proof, encrypted digital contracts serving as verifiable proof of user instructions [8]. - The protocol supports various payment types, including credit cards, debit cards, stablecoins, and real-time bank transfers, ensuring a consistent and secure experience for users and merchants [7]. Group 3: Use Cases and Collaborations - AP2 allows users to delegate tasks to agents, such as booking flights and hotels, with the agent automatically executing transactions once predefined conditions are met [10]. - Google has partnered with over 60 companies, including American Express, Alibaba, and PayPal, to implement the AP2 protocol [10]. Group 4: Technical Implementation - The AP2 project is publicly available on GitHub, including technical specifications, documentation, and reference implementations for developers [12]. - Users are required to have Python 3.10 or higher and must obtain a Google API key to set up the environment for running the protocol [13].

Core Viewpoint - Google has launched the Agent Payments Protocol (AP2), an open shared protocol designed to facilitate secure and compliant transactions between agents and merchants, providing a common language for these interactions [2][10]. Summary by Sections Introduction of AP2 - AP2 serves as an extension of the A2A and MCP protocols, enhancing the capabilities of AI agents in processing payments across platforms [5][7]. - The protocol addresses the need for intelligent interactions among multiple agents, moving beyond manual operations to a more automated and integrated approach [6]. Key Issues Addressed by AP2 - AP2 focuses on three main issues: authorization, authenticity, and accountability in transactions initiated by agents [9]. - It aims to ensure that transactions are secure and that users' intentions are accurately represented, while also establishing clear accountability in case of fraud or errors [8][10]. Operational Mechanism - The protocol utilizes mandates (authorization documents) to build trust, which are tamper-proof, encrypted digital contracts serving as verifiable proof of user instructions [12]. - These mandates create an audit trail from user intent to payment, addressing key concerns of authorization and authenticity [13]. Practical Applications - AP2 enables a new business model in the AI era, allowing agents to interact with various service providers seamlessly. For example, a user can instruct an agent to book travel arrangements within a specified budget, and the agent can execute transactions across multiple platforms [14]. - Google has partnered with over 60 companies, including major players like American Express, Alibaba, and PayPal, to implement this protocol [14]. Technical Implementation - The project is publicly available on GitHub, including technical specifications and reference implementations, facilitating broader adoption and integration [15][24]. - The protocol supports various payment types, ensuring a consistent and secure experience for users and merchants alike [10].