Core Insights - The 2025 World Artificial Intelligence Conference (WAIC) highlighted the growing interest in AI agents, with companies like JD.com, Baidu, and Amazon showcasing their products, indicating a shift towards practical applications of AI in the workplace [1][4] - The focus is shifting from whether AI agents will replace human workers to how they can effectively assist in specific tasks, enhancing human-machine collaboration [1][5] - There is a notable trend towards vertical-specific AI agents that can handle defined tasks, making them more attractive for commercial orders compared to general-purpose agents [2][4] Group 1 - The term "Agent" emerged as a key topic at WAIC, with various companies presenting their AI solutions aimed at assisting rather than replacing human workers [1][4] - Companies are developing AI agents that can perform specific tasks, such as sentiment analysis and document writing, which are seen as more viable for business applications [2][3] - The quality of data processing, particularly vectorization, is crucial for the effectiveness of AI agents in understanding and executing tasks [3][4] Group 2 - There is a growing concern about the sustainability of general-purpose AI models, with industry experts suggesting that only those with clear industry applications will survive [4][5] - Companies like JD.com and Baidu are taking steps to create more user-friendly AI tools, with JD.com open-sourcing its JoyAgent, which has garnered significant interest from developers [3][4] - Despite the enthusiasm for AI technology, companies are cautious, preferring to use AI agents as supportive tools rather than full replacements for human roles [5][6] Group 3 - The market for AI agents is expanding, with significant investment flowing into startups, totaling approximately $700 million by mid-2025 [4][8] - However, there are challenges, including the need for stability, data security, and the ability to meet specific business requirements, which are critical for successful implementation [5][8] - The current AI agent ecosystem lacks a standout product, and companies are exploring hardware integrations to enhance the commercial viability of AI agents [9][4]

以下文章来源于智能涌现 ，作者邓咏仪 智能涌现 . 文 ｜ 邓咏仪 编辑 ｜ 苏建勋 来源｜ 智能涌现（ID： AIEmergence） 封面来源 ｜ AI生成 大模型的风，如今又刮到了一个新名词上：MCP。 AI圈中不缺新鲜事，但这次不一样，互联网仿佛又回到了十多年前的春天。 "现在，基于MCP开发智能体，就像2010年开发移动APP。" 4月25日，百度 董事长李彦宏在百度Create大会上说到。 如果还没有听过MCP，但你肯定听过上一个热词：Agent（智能体）。2025年初，中国初创公司Manus的爆火，把这个名词瞬间推到了大众面前。 "真·能干活的AI"，是Agent爆火的关键。在这之前，大模型可以答疑解惑，但它只是一个简单的对话窗口，依赖于模型接受过的训练，大模型内的数据往 往不是最新的，如果只有大模型本体，调用外部工具，要经历非常繁琐的过程。 MCP这个概念，就和Agent密不可分。 MCP是Agent愿景得以实现的的重要路径——大模型可以自由地调用支持MCP协议的外部工具，完成更具体的任 务。 现在，包括高德地图、微信读书在内的应用，就已经纷纷推出官方的MCP Server（服务器），这意味着 ...

Core Viewpoint - The emergence of MCP (Model Context Protocol) is seen as a pivotal development in the AI industry, akin to the rise of mobile apps in 2010, enabling more efficient interactions between large models and external tools [1][2]. Group 1: Definition and Importance of MCP - MCP is an open standard that allows large models to interact with external data sources and tools, similar to a universal interface like USB [6][12]. - The adoption of MCP is expected to lead to a significant explosion in AI applications by 2025, as it simplifies the development process for AI applications [5][12]. Group 2: Current Trends and Adoption - Since February 2024, a global wave of MCP adoption has occurred, with major companies like OpenAI, Google, and others announcing support for the protocol [2][16]. - Over 4,000 MCP servers have been launched globally, indicating rapid growth in the ecosystem [12]. Group 3: Developer Experience and Challenges - Prior to MCP, developers faced high barriers in integrating external tools with large models, often requiring extensive coding and adaptation [10][11]. - With MCP, developers can focus on maintaining their applications rather than managing external tool performance, significantly reducing development workload [12][13]. Group 4: Competitive Landscape and Strategic Shifts - The shift towards MCP represents a strategic pivot for major AI companies, moving from isolated development to a more collaborative ecosystem [17][21]. - OpenAI's previous closed strategy has been contrasted with MCP's open approach, highlighting the advantages of a more inclusive development environment [18][21].

大模型作为产业变革的核心引擎。通过RAG、Agent与多模态技术正在重塑AI与现实的交互边界。三者协同演进，不仅攻克了数据时效性、专业适配等核 心挑战，更推动行业从效率革新迈向业务重构。本文将解析技术演进脉络、实战经验与未来图景，为读者提供前沿趋势的全局视角与产业升级的实践指 引。 作者 | 蒋进 出品丨腾讯云开发者 大模型技术正加速渗透至产业核心场景，成为驱动数字化转型的智能引擎。全球机器学习大会（ML-Summit）聚焦大模型技术的创新突破与产业实 践，深入探讨其前沿方向与落地路径。作为AI发展的核心驱动力， 检索增强生成（RAG） 通过动态知识融合技术突破大模型的静态知识边界； 智能体 （Agent） 借助自主决策与多任务协同能力重构人机协作范式； 多模态大模型 则依托跨模态语义理解技术解锁复杂场景的落地潜力。三者协同演进， 不仅攻克了数据时效性、隐私安全与专业适配等关键难题，更在医疗诊断、金融风控、智能制造等领域催生从效率革新到业务重构的行业级变革。 ML-Summit会议大模型内容分布 RAG： 大模型的动态知识引擎，解决模型静态知识边界、时效性与可信度问题。 大模型在很多领域表现出色，但依然存在局 ...