Core Viewpoint - The article emphasizes that 2025 marks a significant turning point for AI Agents, transitioning from "toys" to "tools" as various successful Agent projects emerge and major companies release MCP protocol support [1]. Group 1: Development and Features of AI Agents - Many Agent projects are still stuck in the POC stage, facing challenges such as long development cycles and difficulty in validating commercial value [2]. - Alibaba Cloud's new upgrade of Bailian 3.0 provides a comprehensive solution for developers, addressing all needs for large model applications and Agent development [2][12]. - The introduction of the "Agent tipping" feature allows users to reward Agents they find useful, enabling direct monetization for developers [3][4][5]. Group 2: Agent Store and Templates - The Agent Store has officially launched, offering hundreds of Agent templates across various industries, allowing developers to quickly start secondary development projects [7][10][18]. - Developers can easily copy Agent configurations and validate their usability, streamlining the development process [21]. Group 3: Enhanced Capabilities and Tools - The upgrade includes a full suite of capabilities from model supply to application data and development tools, enhancing the overall development experience [13][15]. - The new multi-modal RAG capability supports processing complex enterprise documents, significantly improving document handling capabilities [29][30]. - The introduction of V-RAG allows for better content recognition in structured documents, enhancing the effectiveness of document processing [33][34]. Group 4: MCP Service Enhancements - The MCP service has been upgraded to support KMS encryption, addressing key management issues and reducing risks associated with plaintext exposure [36][37]. - Over 50 enterprise-level MCPs have been launched, with more than 22,000 users utilizing these services to create over 30,000 MCP Agents [41]. Group 5: Multi-modal Interaction Development Kit - The multi-modal interaction development kit provides low-cost development capabilities for enterprises, enabling a new generation of intelligent user experiences [45]. - This kit supports various devices and applications, allowing for flexible integration of multi-modal capabilities [47][48]. Group 6: Commercialization and Sustainability - The introduction of the Agent tipping feature opens new pathways for developers to monetize their creations, establishing a sustainable ecosystem for AI Agents [50][51]. - Alibaba Cloud's exploration serves as a reference for the industry, showcasing a viable commercialization model for AI applications [52].

Group 1 - The article discusses the evolution of AI technology and its integration into various devices, transitioning from PCs and smartphones to future applications in AR/VR, smart cars, drones, and humanoid robots [1][2][4]. - It highlights the shift from traditional input methods, such as physical keyboards, to voice-based interactions, particularly among different age groups [5][6]. - The article raises questions about the relevance of enterprise management software in an era where input and output capabilities are diminishing with new technologies [6][7]. Group 2 - The transition from search engines to AI models for information retrieval is emphasized, with companies adapting their content to be more accessible to AI models through MCP (Model Compatibility Protocol) [8][10]. - The article notes the trend of companies globally opening APIs and integrating with public AI models, while in China, there is a focus on private deployments of domestic models like DeepSeek [15][16]. - It categorizes enterprise personnel into three layers: decision-making, management, and frontline execution, and discusses the use of AI in enhancing decision-making and execution processes [18][19]. Group 3 - The article explores the relationship between AGI (Artificial General Intelligence) and AIGC (AI Generated Content), suggesting that both capabilities are being developed simultaneously [20]. - It identifies AI-generated code as a critical intersection of AGI and AIGC capabilities, allowing AI to expand its functionalities [22]. - The challenges of traditional enterprise management software, particularly the inflexibility of hard-coded solutions, are discussed, along with a new approach using AI to generate and optimize code for specific tasks [23][24][26].

Core Insights - The discussion at the LangChain Agent Conference highlighted the evolution of Agentic systems and the importance of focusing on the degree of Agentic capability rather than simply categorizing systems as "Agents" [2][3][4] - Andrew Ng emphasized the need for practical skills in breaking down complex processes into manageable tasks and establishing effective evaluation systems for AI systems [8][10][12] Group 1: Agentic Systems - The conversation shifted from whether a system qualifies as an "Agent" to discussing the spectrum of Agentic capabilities, suggesting that all systems can be classified as Agentic regardless of their level of autonomy [4][5] - There is a significant opportunity in automating simple, linear processes within enterprises, as many workflows remain manual and under-automated [6][7] Group 2: Skills for Building Agents - Key skills for building Agents include the ability to integrate various tools like LangGraph and establish a comprehensive data flow and evaluation system [8][9] - The importance of a structured evaluation process was highlighted, as many teams still rely on manual assessments, which can lead to inefficiencies [10][11] Group 3: Emerging Technologies - The MCP (Multi-Context Protocol) is seen as a transformative standard that simplifies the integration of Agents with various data sources, aiming to reduce the complexity of data pipelines [21][22] - Voice technology is identified as an underutilized component with significant potential, particularly in enterprise applications, where it can lower user interaction barriers [15][19] Group 4: Future of AI Programming - The concept of "Vibe Coding" reflects a shift in programming practices, where developers increasingly rely on AI assistants, emphasizing the need for a solid understanding of programming fundamentals [23][24] - The establishment of AI Fund aims to accelerate startup growth by focusing on speed and deep technical knowledge as key success factors [26]

Group 1: Global AI Outlook - The report highlights a new paradigm in AI development characterized by new scaling, new architecture, and new total addressable market (TAM) opportunities [1] - The demand for computing power is expected to rise due to advancements in both training and inference processes, potentially unlocking new TAMs [1][3] - The report maintains a positive outlook on AI industry investments, anticipating that global AI applications will enter a performance harvesting phase [1] Group 2: Model Development - The pre-training scaling law is anticipated to open a new starting point for model development, with significant innovations in architecture being explored [2][23] - The report notes that the classic transformer architecture has reached a parameter scale bottleneck, with existing public data nearly exhausted [2][20] - Major tech companies are experimenting with new architectures, such as Tencent's Hunyuan TurboS and Google's Gemini Diffusion, which may accelerate scaling law advancements [23][24] Group 3: Computing Power Demand - The report identifies a clear long-term upward trend in computing power demand, driven by both training and inference needs [3][32] - New scaling paths are emerging in the post-training phase, with ongoing exploration of new architectures that may reignite pre-training demand narratives [3][33] - The deployment of large-scale computing clusters, such as OpenAI's StarGate, is expected to support the exploration of pre-training [38] Group 4: Application Development - The report indicates that the rapid advancement of agent applications is leading to a performance harvesting phase for global AI applications [4][67] - The commercialization of agent products is accelerating, with domestic AI applications quickly iterating and entering the market [4][67] - The report emphasizes that agent applications are evolving from simple tools to complex solutions, with significant growth expected in various sectors [5][68] Group 5: Business Model Transformation - The shift from traditional software delivery to outcome-based delivery is highlighted as a key trend, with quantifiable ROI accelerating the adoption of agent applications [5] - Specific sectors such as consumer-facing scenarios (advertising, e-commerce) and AI in marketing/sales are expected to lead in commercialization due to their inherent advantages [5][67] - The report notes that AI applications in HR are transitioning from efficiency tools to strategic hubs, indicating a broader transformation in business models [5][67]

Core Insights - The release of Veo 3 has generated significant buzz on social media platforms, indicating a growing interest in AI-generated content, with even established platforms like Instagram and TikTok being affected [1][2] - The domestic internet landscape shows a different response to AI, with platforms like Kuaishou leading in usage but not experiencing the same viral spread of AI content as seen abroad [1][2] - Companies are shifting focus from merely showcasing AI capabilities to making AI more accessible and understandable for the general public [2][3] Group 1: AI Application and Industry Trends - The application of AI in niche markets is accelerating, with more consumer-grade products emerging, mirroring the development patterns of mobile internet [2][3] - Worthbuy Technology's strategic embrace of AI is evident in its recent developments, aiming to transform user shopping decision-making processes [2][3] - The contrasting approaches of major e-commerce platforms like Alibaba and Pinduoduo highlight the diverse strategies within the industry regarding AI integration [3][5] Group 2: Worthbuy Technology's AI Strategy - Worthbuy Technology's AI strategy focuses on enhancing the efficiency of connections between B-end and C-end users, reflecting a commitment to improving user experience [6][10] - The "Fire Eye" AIUC engine is central to Worthbuy's AI efforts, enhancing the understanding and extraction of product and content information, thereby streamlining user decision-making [7][8] - The introduction of the MCP Server aims to standardize interactions between AI agents and tools, facilitating a more integrated AI ecosystem within the e-commerce sector [11][14] Group 3: User-Centric Approach - Worthbuy's commitment to user engagement is evident in its product upgrades and the development of its agent "Zhang Dama," which reflects the company's historical focus on user needs [16] - The company's strategy emphasizes the importance of community and user feedback, which has been a consistent theme throughout its evolution in the e-commerce landscape [16]

作者 | 褚杏娟 对于业内讨论的一些问题，实在智能通过自身实践也给出了自己的答案。比如自研模型或垂直模型对于具体业务场景中的 Agent 研发是必要的，但大模 型自身并不能作为一种产品。又如，在支持 MCP 后，实在智能也发现不能过度依赖 MCP 服务，MCP 只是将一些问题进行了封装，但问题本质并没有 得到解决。 当下，智能体的热度已经无需再多赘述。这场智能体竞赛中，除了那些从新开始的"AI 原生"智能体应用外，还有一些应用在逐渐将智能体纳入产品构建 中，实在智能便是其中之一。 实在智能成立于 2018 年7月，以RPA为起点，融合AI技术，致力于通过人工智能技术助力人机协同，提供超自动化解决方案。随着技术发展，实在智能 对其"数字员工"产品不断升级：对RPA的底层能力做了大量的改造和增强，结合计算机视觉对底层架构进行了重构，并推出了国内首款通用智能体产 品。当前，实在智能已为超 4000 家企业客户部署了"数字员工"。 近日，InfoQ 对实在智能创始人兼 CEO 孙林君进行了一次采访，期间他详细回答了智能体技术路径选择、产品如何转型、智能体产品收费逻辑等问题。 智能体的实现路径 InfoQ：2018 年 ...

Group 1 - The development of AI Agents has accelerated significantly since 2025, with notable acquisitions and funding rounds, such as OpenAI's $3 billion acquisition of Windsurf and Anysphere's $900 million funding round, valuing Cursor at $9 billion [1][3] - The emergence of various platforms and tools, such as MindOS and Second Me, indicates a growing trend towards creating personalized AI Agents, reflecting a shift in the industry towards more accessible development [4][6] - The definition of AI Agents has evolved, now characterized by their ability to perform tasks independently, driven by large language models, and equipped with memory systems and user interaction interfaces [6][8] Group 2 - The integration of reasoning models and Reinforcement Fine-Tuning (RFT) technology has enabled AI Agents to learn and adapt in specific domains, marking a significant advancement in their capabilities [8][15] - The distinction between traditional reinforcement learning Agents and modern AI Agents lies in their ability to learn from environments, with the latter now capable of autonomous learning and exploration [12][14] - The competitive landscape for AI Agents is shifting, with companies like Cursor and Windsurf leading the charge due to their deeper understanding of environments and user needs [18][20] Group 3 - The rise of AI Agents has created both opportunities and challenges for entrepreneurs, as the market becomes saturated with service-oriented Agents, making it difficult for new entrants to find unique value propositions [22][23] - The importance of model capabilities, engineering skills, and data barriers is highlighted as key competitive advantages in the AI Agent space, with the performance of models like Claude Sonnet 3.7 being pivotal for success [25][28] - The future of AI Agents may see a convergence of programming tools and general-purpose Agents, as companies like Cursor and Windsurf begin to integrate broader functionalities [31][55] Group 4 - The industry is experiencing a rapid pace of development, with a shift towards faster execution and less emphasis on detailed planning documents, reflecting a more agile approach to product development [64][66] - Despite the excitement around AI Agents, significant challenges remain in achieving widespread adoption and understanding user needs effectively, indicating that the journey towards mainstream usage is still ongoing [68][71] - The MCP protocol, which governs how AI Agents access external information, is still in its early stages and requires industry-wide acceptance to fully realize its potential [71][73]

Core Viewpoint - The article emphasizes the importance of distinguishing between LLM Inference and LLM Serving, as the rapid development of LLM-related technologies has led to confusion in the industry regarding these concepts [1][3]. Summary by Sections LLM Inference and LLM Serving Concepts - LLM Inference refers to the process of running a trained LLM to generate predictions or outputs based on user inputs, focusing on the execution of the model itself [5]. - LLM Serving is oriented towards user and client needs, addressing the challenges of using large language models through IT engineering practices [7]. Characteristics and Responsibilities - LLM Inference is computation-intensive and typically requires specialized hardware like GPUs or TPUs [4]. - The responsibility of LLM Inference includes managing the model's runtime state and execution, while LLM Serving encompasses end-to-end service processes, including request handling and model management [10]. Technical Frameworks - vLLM is highlighted as a typical implementation framework for LLM Inference, optimizing memory usage during service inference [5][7]. - Kserve is presented as an example of LLM Serving, providing capabilities for model versioning and standardized service experiences across different machine learning frameworks [7][10]. Model Context Protocol (MCP) - MCP is described as a standardized protocol that connects AI models to various data sources and tools, functioning as a bridge between LLM Inference and LLM Serving [11][12]. - The architecture of MCP suggests that it plays a role similar to LLM Serving while also addressing aspects of LLM Inference [12][16]. Future Development of MCP - The article predicts that MCP will evolve to enhance authentication, load balancing, and infrastructure services, while clearly delineating the functions of LLM Inference and LLM Serving [17].

Core Insights - The article discusses the risks associated with the Multi-Agent Collaboration Protocol (MCP), particularly the potential for tool poisoning attacks that could manipulate AI agents to perform unauthorized actions [1][8][9] - The emergence of AI agents is highlighted as a transformative trend, with predictions indicating that by 2028, at least 15% of daily work decisions will be made autonomously by AI agents [2][4] - The commercial viability of AI agents is emphasized, with a focus on their ability to meet consumer needs and create a self-sustaining economic cycle [3][10] Group 1: Agent Ecosystem and Trends - The development of AI agents is expected to either replace traditional applications or enhance them with intelligent, proactive capabilities [2][4] - The introduction of DeepSeek has accelerated the adoption of AI agents, with a notable increase in inquiries and revenue generation in the industry [3][10] - The transition from single assistants to collaborative networks of agents is anticipated, leading to the formation of an "Agent Economy" [4][9] Group 2: Security Risks and Challenges - Security challenges are identified as critical for the stable operation of agent systems, with vulnerabilities in the MCP protocol posing significant risks [7][9] - Tool poisoning attacks (TPA) are highlighted as a major concern, where attackers can embed malicious instructions within the MCP code, leading to unauthorized actions by AI agents [8][9] - The lack of adequate security mechanisms during the design phase of protocols like MCP and A2A has resulted in hidden vulnerabilities that could be exploited [9][12] Group 3: Safety Measures and Industry Response - The industry is urged to implement proactive security measures across the entire value chain to mitigate risks associated with AI agents [11][12] - The responsibility for security varies depending on the application context, with general SaaS products having different security obligations compared to industry-specific applications [11][12] - Collaboration between AI model developers and security firms is essential to address both internal and external security challenges in the deployment of AI agents [12][13]

作者 | AICon 全球人工智能开发与应用大会 策划 | 李忠良 编辑 | 宇琪 随着技术的快速发展，大模型在各行业的应用潜力日益凸显，但如何将大模型能力高效转化为实际业 务价值，仍是企业面临的核心挑战。 近日 InfoQ《极客有约》X AICon 直播栏目特别邀请了 华为云 AI 应用首席架构师郑岩 担任主持人， 和 蚂蚁集团高级技术专家杨浩、明略科技高级技术总监吴昊宇 一起，在 AICon 全球人工智能开发 与应用大会 2025 上海站 即将召开之际，共同探讨大模型如何驱动业务提效。 部分精彩观点如下： 在 5 月 23-24 日将于上海举办的 AICon 全球人工智能开发与应用大会 上，我们特别设置了 【大模型 助力业务提效实践】 专题。该专题将围绕模型选型与优化、应用场景落地及效果评估等关键环节，分 享行业领先企业的实战经验。 查看大会日程解锁更多精彩内容： https://aicon.infoq.cn/2025/shanghai/schedule 以下内容基于直播速记整理，经 InfoQ 删减。 场景探索 郑岩：在探索大模型应用场景时，企业常会遇到"看起来很美但落地难"的需求，各位在实际项目中是 ...