作者 | 黄昱 2025年AI应用爆发，同时迎来Agent元年等背景下，推理需求暴涨。为了抓住这一机遇，云服务厂商也积极升级云基础设施，来满足市场需求。 9月16日，在2025腾讯全球数字生态大会上，腾讯集团副总裁、腾讯云总裁邱跃鹏表示，大模型产业重心从训练到推理的转变，已经成为行业共识。同时客 户对于使用大模型和建设Agent迸发出强烈热情，这都带来了推理需求的暴涨。 这也意味着，AI基础设施要同步升级。 近年来，腾讯云正不断升级云基础设施，以支撑Agent规模化落地和企业全球化发展。据邱跃鹏介绍，腾讯云已在推理加速、Agent Infra和国际化布局等方 面取得突破，并将以更加开放的姿态，助力企业把握时代机遇。 在推理加速方面，腾讯云深入参与开源贡献，向DeepSeek、vLLM、SGLang等社区提交了多项优化技术。同时，针对大模型推理面临的内存瓶颈，腾讯云自 研并开源FlexKV 多级缓存技术，大幅降低KVCache的占用，将首字时延降低多达70%。 同时，邱跃鹏透露，腾讯云依托异构计算平台整合多种芯片资源，向外界提供高性价比的 AI 算力。目前，该平台已全面适配主流国产芯片。 据悉，软硬件协同全栈优 ...

Core Insights - Tencent Cloud is actively participating in the open-source community and has developed a heterogeneous computing platform that integrates various chip resources to provide cost-effective AI computing power [1][5] - The shift in the large model industry from training to inference has led to a surge in demand for inference capabilities, prompting upgrades in AI infrastructure [3][4] - Tencent Cloud's infrastructure now covers 55 availability zones globally, with over 3,200 acceleration nodes, and has successfully defended against a 183% year-on-year increase in DDoS attacks [1][10] Group 1: AI Infrastructure and Optimization - Tencent Cloud has contributed multiple optimization technologies to open-source communities, including FlexKV multi-level caching technology, which reduces KVCache usage and lowers first-word latency by up to 70% [1][4] - The company has optimized GPU communication performance by 30% and doubled performance in common data center environments through enhancements to the DeepEP technology [3][4] - The introduction of the Agent Runtime solution provides a secure and efficient environment for deploying AI agents, integrating various components such as execution engines and cloud sandbox services [5][6] Group 2: Global Expansion and Client Support - Tencent Cloud has established nine technical support centers globally and plans to build new availability zones in Osaka, Japan, and Saudi Arabia, enhancing its international presence [1][14] - The company successfully migrated a large-scale project for GoTo, Indonesia's largest tech group, completing over 500 customized requirements and establishing a third availability zone in just five months [14] - Tencent Cloud's services have been recognized as a leader in the global gaming cloud platform market, providing robust infrastructure to support over 10,000 games and ensuring low-latency experiences for players worldwide [10][11] Group 3: Advanced Technologies and Services - The Cloud Mate service, which consists of various sub-agents, enhances cloud governance and risk management, achieving a 95% interception rate for risky SQL queries [8][9] - The integration of AI with database optimization has resulted in an 80% reduction in total latency for complex queries, showcasing Tencent Cloud's commitment to improving performance [9][10] - The EdgeOne product, which combines AI and security acceleration, has facilitated over 100,000 users in deploying e-commerce web pages quickly and efficiently [11][12]

Core Insights - The article discusses the evolution of AI, particularly focusing on the transition to the "second half" of AI development, emphasizing the importance of language and reasoning in creating more generalizable AI systems [4][62]. Group 1: AI Evolution and Language - The concept of AI has evolved from rule-based systems to deep reinforcement learning, and now to language models that can reason and generalize across tasks [41][43]. - Language is highlighted as a fundamental tool for generalization, allowing AI to tackle a variety of tasks by leveraging reasoning capabilities [77][79]. Group 2: Agent Systems - The definition of an "Agent" has expanded to include systems that can interact with their environment and make decisions based on reasoning, rather than just following predefined rules [33][36]. - The development of language agents represents a significant shift, as they can perform tasks in more complex environments, such as coding and internet navigation, which were previously challenging for AI [43][54]. Group 3: Task Design and Reward Mechanisms - The article emphasizes the importance of defining effective tasks and environments for AI training, suggesting that the current bottleneck lies in task design rather than model training [62][64]. - A focus on intrinsic rewards, which are based on outcomes rather than processes, is proposed as a key factor for successful reinforcement learning applications [88][66]. Group 4: Future Directions - The future of AI development is seen as a combination of enhancing agent capabilities through better memory systems and intrinsic rewards, as well as exploring multi-agent systems [88][89]. - The potential for AI to generalize across various tasks is highlighted, with coding and mathematical tasks serving as prime examples of areas where AI can excel [80][82].

Core Insights - The article discusses the evolution of AI, particularly focusing on the development of personal agents like Macaron, which aims to enhance user experience by understanding individual preferences and needs through memory and reinforcement learning (RL) [2][6][12]. Group 1: Product Development and Features - Macaron is designed as a personal agent that goes beyond productivity tools, aiming to assist users in their daily lives by understanding their preferences and providing personalized solutions [13][14]. - The product emphasizes strong memory capabilities, allowing it to remember user preferences and provide tailored suggestions, such as meal planning based on dietary restrictions [15][16]. - The development of Macaron involves multi-agent systems, where memory agents and coding agents are trained separately to balance emotional intelligence and practical functionality [3][24]. Group 2: Training and Technology - Memory is treated as a method to enhance user service rather than an end goal, with a focus on how well the agent can assist users based on remembered information [15][16]. - The use of All-Sync RL technology accelerates the training process, allowing for faster iterations and improvements in the agent's capabilities [3][39]. - The company has implemented a unique database structure that allows all sub-agents to share the same personal data, enhancing the overall functionality and user experience [32]. Group 3: User Engagement and Community - The onboarding process for new users includes personality tests and personalized interactions to create a sense of companionship, akin to a friend rather than just a tool [21][22]. - Macaron aims to build a community where users can share their unique lifestyles and preferences, allowing for the creation of sub-agents that reflect individual habits and interests [26][28]. - The company recognizes the importance of user feedback in refining its offerings, with plans to enhance the speed and stability of its applications based on early user experiences [54][55]. Group 4: Market Position and Future Outlook - The company positions Macaron not as a traditional app store but as a personal agent capable of unlocking significant commercial potential by integrating into users' daily lives [60]. - The focus on lifestyle integration rather than just productivity tools is seen as a key differentiator in the market, with the potential for greater value creation through the aggregation of various life scenarios [60]. - Future developments may include innovative business models that reward users for sharing their agents and experiences within the community, moving beyond a subscription-based model [60].

Core Insights - The emergence of DeepSeek R1 has significantly reduced the cost of inference models while maintaining performance close to the best models available, indicating a potential for increased demand as costs decrease [1] - The launch of ChatGPT marked a pivotal moment, with its daily active users nearing 30% of search engine usage by March this year, highlighting the integration of large models into daily life [1] - The rapid improvement in model performance and reduction in usage costs are expected to continue, driving the development of large models and their impact on various industries [1] - The concept of agents is evolving, with their planning capabilities growing exponentially, suggesting a new phase in AI development referred to as Moore's Law 3.0, where agent capabilities double every seven months [1] - AI is transitioning from being an assistant to becoming a partner, indicating a shift in the relationship between humans and machines, which will alter organizational structures and employment in the future [2]

Core Viewpoint - The article discusses the emergence of Agent AI, highlighting its potential to revolutionize various fields through a new cognitive architecture that integrates perception, cognition, action, learning, and memory [4][9][10]. Summary by Sections Introduction to Agent AI - 2025 is anticipated to be the year of Agent AI, with increasing interest in concepts like AI Agents and Agentic AI [4]. - A significant paper led by Fei-Fei Li titled "Agent AI: Surveying the Horizons of Multimodal Interaction" has sparked widespread discussion in the industry [4][6]. Framework of Agent AI - The paper establishes a clear framework for Agent AI, integrating various technologies into a unified perspective [6][7]. - It outlines five core modules: Environment and Perception, Cognition, Action, Learning, and Memory, which together form a dynamic cognitive loop [10][12][14][16][17]. Core Modules Explained - **Environment and Perception**: Agents actively perceive information from their surroundings, incorporating task planning and skill observation [12]. - **Cognition**: Acts as the processing center, utilizing large language models (LLMs) and visual language models (VLMs) for reasoning and strategy formulation [14]. - **Action**: Converts cognitive decisions into executable commands, affecting the environment [15]. - **Learning**: Emphasizes continuous learning through various mechanisms, allowing agents to adapt based on feedback [16]. - **Memory**: Features a structured system for long-term knowledge retention, enabling agents to leverage past experiences [17]. Role of Large Models - The development of Agent AI is driven by the maturity of foundation models, particularly LLMs and VLMs, which provide agents with extensive knowledge and planning capabilities [20]. - The paper addresses the challenge of "hallucination" in models, emphasizing the importance of environmental interaction to mitigate this issue [21][22]. Application Potential - The paper explores Agent AI's applications in three key areas: - **Gaming**: Agent AI can create dynamic NPCs that interact meaningfully with players, enhancing immersion [24][25]. - **Robotics**: Robots can execute complex tasks based on natural language commands, improving user interaction [27]. - **Healthcare**: Agent AI can assist in preliminary diagnostics and patient monitoring, increasing efficiency in healthcare delivery [29][31]. Conclusion - The paper recognizes that Agent AI is still in its early stages, facing challenges in integrating multiple modalities and creating general agents for diverse applications [32]. - It proposes new evaluation benchmarks to guide the development and measure progress in the field [32].

Core Insights - The article emphasizes the significance of the "Attention" mechanism in AI development, highlighting its role as a foundational paradigm that transcends mere model components [1][6] - The company has initiated a series of deep interviews focusing on "Attention" to explore its implications in AI and its intersection with human cognition [5][12] Group 1: AI Development and Attention Mechanism - The past seven years have seen "Attention" as a common underlying theme in key advancements in AI technology [1] - The company believes that the current AI innovations represent a transformative wave, surpassing the scale of the Industrial Revolution [1] - The exploration of "Attention" is not merely a retrospective but a necessary discussion to understand its relevance in today's AI landscape [6] Group 2: AI Portfolio and Research Initiatives - The company has built a core investment portfolio in AI and embodied intelligence, including nearly twenty projects such as MiniMax and Vast [1] - The first deep interview series focused on understanding the essence of AI and its foundational technologies, leading to insights about AI as a future infrastructure [2][3] - The second series centered on "Agent," exploring its role as a service driven by large models, emphasizing its importance in the AI ecosystem [4] Group 3: Future Directions and Human Cognition - The article discusses the dual evolution of AI, where scholars are working on both scaling Transformer structures and innovating cognitive frameworks to enhance AI's understanding of "Attention" [8] - It raises critical questions about the implications of AI's evolution on human attention mechanisms, especially in a world increasingly filled with fragmented information [10][11] - The company aims to protect human attention while helping AI learn to manage it, marking the beginning of a new series of discussions on this topic [12]

Core Insights - The article discusses the rising prominence of Agent AI, with 2025 being viewed as a pivotal year for this technology [1][2] - A significant paper led by Fei-Fei Li titled "Agent AI: Surveying the Horizons of Multimodal Interaction" has sparked extensive discussion in the industry [3][6] Summary by Sections Overview of the Paper - The paper, consisting of 80 pages, provides a clear framework for the somewhat chaotic field of Agent AI, integrating various technological strands into a new multimodal perspective [5][6] - It emphasizes the evolution from large models to agents, reflecting the current strategies of major players like Google, OpenAI, and Microsoft [6] New Paradigm of Agent AI - The paper introduces a novel cognitive architecture for Agent AI, which is not merely a compilation of existing technologies but a forward-thinking approach to the development of Artificial General Intelligence (AGI) [9] - It defines five core modules: Environment and Perception, Cognition, Action, Learning, and Memory, which together form an interactive cognitive loop [10][26] Core Modules Explained - **Environment and Perception**: Agents actively perceive information from their surroundings in a multimodal manner, incorporating various data types [12][13] - **Cognition**: Acts as the processing center for agents, enabling complex activities such as reasoning and empathy [15][16] - **Action**: Converts cognitive decisions into specific operational commands, affecting both physical and virtual environments [18][19] - **Learning**: Highlights the continuous learning and self-evolution capabilities of agents through various mechanisms [20][21] - **Memory**: Offers a structured system for long-term knowledge retention, allowing agents to leverage past experiences for new tasks [23][24] Role of Large Models - The framework's feasibility is attributed to the maturity of large foundational models, particularly LLMs and VLMs, which provide essential cognitive capabilities for agents [28][29] - These models enable agents to decompose vague instructions into actionable tasks, significantly reducing the complexity of task programming [30][31] Challenges and Ethical Considerations - The paper identifies the issue of "hallucination" in models, where they may generate inaccurate content, posing risks in real-world interactions [32][33] - It emphasizes the need for inclusivity in designing Agent AI, addressing biases in training data and ensuring ethical interactions [36][39] - The importance of establishing regulatory frameworks for data privacy and security in Agent AI applications is also highlighted [38][39] Application Potential - The paper explores the vast application potential of Agent AI in gaming, robotics, and healthcare [40] - In gaming, Agent AI can create dynamic NPCs that interact meaningfully with players, enhancing immersion [42][43] - In robotics, agents can autonomously execute complex tasks based on simple verbal commands, streamlining user interaction [48][49] - In healthcare, Agent AI can assist in preliminary diagnostics and patient monitoring, improving efficiency in resource-limited settings [54][57] Future Directions - The paper acknowledges that Agent AI is still in its early stages, facing challenges in integrating multiple modalities and creating general-purpose agents [58][60] - It proposes new evaluation benchmarks to measure agent intelligence and guide future research [61]

Core Insights - The article discusses the evolution and challenges of AI Agents, highlighting their transition from simple chat assistants to more complex digital employees capable of long-term planning and tool usage [5][6] - It emphasizes the importance of context and implicit knowledge in the successful deployment of Agents, particularly in B2B scenarios [8][11] - The article suggests that the focus for entrepreneurs should shift from general-purpose Agents to vertical specialization, addressing specific use cases to enhance user retention and value [24][20] Group 1: Challenges in Agent Development - Implicit knowledge acquisition is a core challenge for Agents, especially in B2B contexts, where understanding business logic and context is crucial for task completion [8][11] - The shift from rule-based workflows to more autonomous Agent capabilities is highlighted, with many past engineering efforts deemed unnecessary due to advancements in model capabilities [10][19] - The article notes that many companies have struggled with the limitations of general-purpose Agents, leading to low retention and conversion rates [23][24] Group 2: Entrepreneurial Focus Areas - Entrepreneurs are encouraged to focus on context engineering to create environments that facilitate the effective deployment of large models [13][15] - The article discusses the choice between targeting large clients (KA) versus small and medium-sized businesses (SMB), with SMBs presenting unique opportunities for rapid product validation and market penetration [21][20] - It suggests that a dual approach of validating products in the SMB market while selectively targeting large clients can be effective [21][20] Group 3: Technical and Commercial Strategies - The article outlines two technical routes for Agent development: workflow-based and agentic, with the latter gaining traction as model capabilities improve [16][19] - It emphasizes the need for a clear understanding of customer workflows to determine the most efficient approach for Agent implementation [16][17] - The discussion includes the importance of building a sustainable context management system that evolves with usage, enhancing the Agent's learning and adaptability [39][47] Group 4: Future Directions and Innovations - The article raises questions about the future of Agents in relation to large models, suggesting that the true competitive advantage lies in deep environmental understanding and continuous learning [36][37] - It highlights the potential for multi-Agent architectures to address complex tasks but notes the challenges in context sharing and task delegation [33][34] - The need for improved memory and learning mechanisms in Agents is emphasized, with suggestions for capturing decision-making processes and user interactions to enhance performance [42][46]

Core Insights - The latest paper by Fei-Fei Li delineates the boundaries and establishes paradigms for the currently trending field of Agents, with major players like Google, OpenAI, and Microsoft aligning their strategies with the proposed capability stack [1][4] - The paper introduces a comprehensive cognitive loop architecture that encompasses perception, cognition, action, learning, and memory, forming a dynamic iterative system for intelligent agents, which is not only a technological integration but also a systematic vision for the future of AGI [1][5] - Large models are identified as the core engine driving Agents, while environmental interaction is crucial for addressing issues of hallucination and bias, emphasizing the need for real or simulated feedback to calibrate reality and incorporate ethical and safety mechanisms [1][3][11] Summary by Sections 1. Agent AI's Core: A New Cognitive Architecture - The paper presents a novel Agent AI paradigm that is a forward-thinking consideration of the development path for AGI, rather than a mere assembly of existing technologies [5] - It defines five core modules: Environment and Perception, Cognition, Action, Learning, and Memory, which together create a complete and interactive cognitive loop for intelligent agents [5][10] 2. How Large Models Drive Agent AI - The framework of Agent AI is made possible by the maturity of large foundational models, particularly LLMs and VLMs, which serve as the basis for the cognitive capabilities of Agents [11][12] - LLMs and VLMs have internalized vast amounts of common and specialized knowledge, enabling Agents to perform zero-shot planning effectively [12] - The paper highlights the challenge of "hallucination," where models may generate inaccurate content, and proposes environmental interaction as a key anchor to mitigate this issue [13] 3. Application Potential of Agent AI - The paper explores the significant application potential of Agent AI in three cutting-edge fields: gaming, robotics, and healthcare [14][19] - In gaming, Agent AI can transform NPC behavior, allowing for meaningful interactions and dynamic adjustments based on player actions, enhancing immersion [15] - In robotics, Agent AI enables users to issue commands in natural language, allowing robots to autonomously plan and execute complex tasks [17] - In healthcare, Agent AI can serve as a medical chatbot for preliminary consultations and provide diagnostic suggestions, particularly in resource-limited settings [19][21] 4. Conclusion - The paper acknowledges that Agent AI is still in its early stages and faces challenges in achieving deep integration across modalities and domains [22] - It emphasizes the need for standardized evaluation metrics to guide development and measure technological progress in the field [22]