模型上下文协议(MCP)

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AI替用户剁手,电商行业这下真要变天了
3 6 Ke· 2025-09-24 12:14
此前在今年年初,我们曾在《AI购物竟是人工驱动,硅谷创投圈又玩出新花活》中提及AI购物赛道独 角兽Nate涉嫌欺诈,其所谓的专有AI技术实现"一键式自动购物",实际上自动化率近乎于零,是依赖海 外数百名人类客服的"人工驱动"。在Nate的这个谎言被戳穿前,该公司在过去七年时间里已经骗取了投 资者超过5000万美元的资金。 为什么投资者会对一个骗局如此着迷?其实是因为Nate讲述的故事,与当年的"滴血验癌"有着异曲同工 之妙,毕竟通过AI来帮助用户实现一键购物,极有可能会重塑电商行业的格局。 如今谷歌和PayPal准备联手将Nate的畅想变为现实,近日双方宣布建立线上购物合作伙伴关系,将各自 的数字化支付模式与AI工具结合用于全球交易体系。 具体来说,谷歌和PayPal将分别拿出各自的AI技术以及支付解决方案,来构建由AI智能体代表消费者完 成从发现商品到下单付款的全流程"代理型商业"(Agentic Commerce)。目前双方正共同制定行业标准, 确保在AI代理参与交易过程中具备充分的风险控制与支付安全机制,以保障商家、消费者,以及AI工 具间的可信交互。 按照PayPal的说法,谷歌的AI技术搭配PayP ...
由红杉 AI 峰会闭门会引发的部分思考
3 6 Ke· 2025-05-22 12:28
Core Insights - The core viewpoint of the summit is the fundamental shift in AI's business logic from "selling tools" to "selling outcomes" [2][4][11] Group 1: AI Business Model Transformation - AI's commercial logic is transitioning from a focus on software functionality to a focus on measurable business outcomes [2][4] - Clients are now more interested in how AI can deliver tangible results rather than just its features [4][11] - This shift necessitates that AI products deeply integrate into clients' business processes to effectively address pain points and deliver results [6][11] Group 2: Rise of Operating System-like AI - The summit highlighted a shift in AI's role from being "called upon" to "actively scheduling tasks" [8][9] - AI is evolving towards an operating system level, where it can remember user preferences and act on their behalf [8][9] - This new interaction model will redefine how users engage with software, emphasizing efficiency and resource allocation [9] Group 3: Emergence of the Agent Economy - The concept of the "agent economy" was introduced, where AI entities can act, make decisions, and collaborate as economic participants [10] - Agents will have persistent identities and capabilities, allowing them to form networks and exchange value [10] - The role of humans is shifting from controllers to orchestrators, designing the responsibilities and interfaces of these agents [10] Group 4: End-to-End Iterative AI Models - End-to-end iterative AI models are showing unique adaptability for businesses, especially for small and medium enterprises [12][13] - These models require lower investment and can be tailored to specific business needs, allowing for continuous iteration and optimization [12][13] Group 5: Model Context Protocol (MCP) - The Model Context Protocol (MCP) is emerging as a key development direction for AI platforms, facilitating connections between AI models and external tools [14][15] - MCP enhances development efficiency and intelligence levels in AI applications across various industries [14] Group 6: Results-Driven Growth - The concept of "results-driven growth" emphasizes a systematic approach to AI application in businesses, focusing on optimizing every process through AI [16] - This model aims to create a closed-loop service experience for users, enhancing their engagement and loyalty [16] Group 7: Explosive Growth of Agents - The agent market is experiencing explosive growth, with various intelligent agents emerging across different sectors [17] - As competition intensifies, agents lacking unique advantages will likely be phased out, leading to a more mature and concentrated market [17] Group 8: Transition to Physical AI Era - The future of intelligent ecosystems is moving towards a physical AI era, integrating real-time data interactions among various intelligent agents [18][19] - This evolution will significantly alter interactions with the physical world, enabling real-time communication and collaboration among devices [19]
AI智能体协议全面综述:从碎片化到互联互通的智能体网络
欧米伽未来研究所2025· 2025-05-06 13:33
Core Viewpoint - The article discusses the evolution and categorization of AI agent protocols, emphasizing the need for standardized communication to enhance collaboration and problem-solving capabilities among AI agents across various industries [1][9]. Summary by Sections AI Agent Protocols Overview - The report introduces a systematic two-dimensional classification framework for existing AI agent protocols, distinguishing between context-oriented protocols and inter-agent protocols, as well as general-purpose and domain-specific protocols [1]. Model Context Protocol (MCP) - MCP represents a centralized approach where a core "MCP travel client" agent coordinates all external services, leading to a star-shaped information flow. While it is simple and easy to control, it lacks flexibility and scalability, making it challenging to adapt to complex tasks [2][3]. Agent-to-Agent Protocol (A2A) - A2A promotes a distributed and collaborative model, allowing agents to communicate directly without a central coordinator. This flexibility supports dynamic responses to changing needs but may face challenges when crossing organizational boundaries [4][5]. Agent Network Protocol (ANP) - ANP standardizes cross-domain interactions, enabling agents from different organizations to collaborate effectively. It formalizes the request and response process, making it suitable for diverse and secure environments [6]. Agora Protocol - Agora focuses on translating user natural language requests into standardized protocols for execution by specialized agents. This three-stage process enhances adaptability and allows agents to concentrate on their core functions [7][8]. Future Trends in AI Agent Protocols - The development of AI agent protocols is expected to evolve towards more adaptive, privacy-focused, and modular systems. Short-term goals include establishing unified evaluation frameworks and enhancing privacy protection mechanisms [9][10]. - Mid-term trends may involve embedding protocol knowledge into large language models and developing layered protocol architectures to improve interoperability [11][12]. - Long-term aspirations include creating a collective intelligence infrastructure and specialized data networks to facilitate structured, intent-driven information exchange among agents [13][14][15]. Conclusion - The exploration of AI agent protocols indicates a clear trajectory towards a more intelligent, autonomous, and collaborative future, with significant implications for technology, society, and economic models [16][17].