今天分享的是：人工智能专题：智能体互联网白皮书 报告共计：53页 《人工智能专题：智能体互联网白皮书》由中国联通研究院与下一代互联网宽带业务应用国家工程研究中心发布，聚焦智能体互联网的发展背景、核心挑 战、系统架构、关键技术、演进路径及应用案例。随着生成式AI与大模型技术突破，智能体（AI Agent）快速涌现，推动互联网从"连接信息"向"连接智 能"范式转变，智能体互联网应运而生，其核心是连接具备自主感知、决策与执行能力的智能体，实现从"连接设备"到"连接智能"的跃迁，目标特征体现在 自主性（智能体可自主决策适应环境）、语义化（网络节点能解析数据语义）、群体智能（多智能体协同完成复杂任务）。现有互联网架构存在网络能力 （缺乏语义处理与上下文共享）、协议能力（固定接口难满足意图驱动通信）、控制模式（中心化控制不适应分布式自治）局限，需突破身份管理、能力注 册发现、新型路由寻址等关键技术，如采用分布式数字身份保障可信交互，构建动态组网与语义传输机制支撑多模态内容交互，通过任务编排与安全机制实 现跨域协同与风险防控。系统架构自下而上分为基础资源层（提供计算、网络等资源）、互联功能层（实现智能体接入、发现与传输）、 ...

Core Insights - AgentUnion has officially launched the Agent Communication Protocol (ACP), which is China's first practical communication protocol for agents [2] - ACP aims to facilitate communication and collaboration among agents, enabling efficient functionality reuse and cost-effective development of production-level agent applications [2][11] - The protocol defines various standards for agent identity, access mechanisms, data specifications, communication protocols, authorization systems, behaviors, and security, ensuring openness, reliability, and collaboration among agents [2][11] Summary by Categories Agent Architecture and Concepts - Each agent in the network is assigned a unique identity identifier known as AID (Agent Identifier), which is essential for accessing the agent internet [2][3] - Agents connect to the agent internet through Access Points (AP), which provide services such as identity authentication, addressing, communication, and data storage [4] - The communication protocol is based on HTTPS, ensuring secure data transmission and supporting large-scale concurrent services for enterprise-level deployments [5] Agent Discovery and Collaboration - ACP is designed to be friendly to agent search engines, allowing agents to easily discover each other without additional effort [7] - The protocol outlines the authorization and transaction processes for agent collaboration, facilitating the deployment of paid agent services [8] Evolution of Agents - The field of artificial intelligence is experiencing a significant leap in understanding agents, transitioning from a focus on chatbots to tools that solve specific business problems [10] - The emergence of ACP is seen as a catalyst for the transition from the tool expansion phase to the socialization phase of agents, enabling standardized communication and cross-domain collaboration [11] - AgentUnion aims to build an open protocol standard and decentralized collaboration network to support the free interconnection, secure interaction, and collaborative evolution of different agents [11]

Core Insights - The article discusses the transformation in the AI industry, emphasizing the shift from individual AI model usage to a collaborative network of agents, termed as "Agent collaboration network" [8][10][27] - It highlights the urgency for AI professionals to adapt their skills from prompt engineering to organizing and managing AI collaborations, as traditional skills may become obsolete [9][21][30] Group 1: Industry Trends - The AI landscape is evolving towards a multi-agent system where agents communicate and collaborate autonomously, moving away from reliance on human prompts [27][14] - The emergence of protocols like MCP (Multi-agent Communication Protocol) and A2A (Agent-to-Agent) is facilitating this transition, allowing for standardized communication between different AI systems [36][37] - Major companies like Alibaba, Tencent, and ByteDance are rapidly developing platforms that support these new protocols, enabling easier integration and deployment of AI agents [38][39] Group 2: Skills Transformation - AI professionals need to transition from being prompt engineers to "intent architects," focusing on defining task languages and collaboration protocols for agents [29][30] - The role of AI practitioners is shifting from using agents to organizing and managing multiple agents, requiring a new mindset akin to building a digital team [30][31] - There is a call for professionals to learn about agent frameworks, communication protocols, and how to register their tools as agent capabilities within larger networks [33][34] Group 3: Practical Applications - Various platforms and frameworks are emerging that allow AI professionals to practice and implement these new skills, such as LangGraph, AutoGen, and CrewAI [41] - The article emphasizes that the infrastructure for agent protocols is being established, providing opportunities for AI professionals to engage with these technologies [41][42] - The ongoing development of these systems is likened to the early days of TCP/IP, suggesting that those who adapt early will have a competitive advantage in the evolving AI landscape [42]