直播时间 12 月 10 日 20:00-21:30 直播主题 从 Chatbot 到 Action Agent，企业级落地最怕什么？是长程推理的显存天价成本，还是业务逻辑的"死循环"风险？如何利用 MCP 协议解决接口调用 的"信任危机"？本次直播集结值得买、商汤、明略三位技术专家拆解可信 Agent 的构建之道。 直播介绍 鲁琲 商汤科技大装置事业群 高级技术总监 王云峰 值得买科技 CTO 吴昊宇 明略科技 高级技术总监 企业 Agent 如何"可信"？ 直播嘉宾 主持人： 马可薇 RBC senior application support analyst 嘉宾： 直播亮点 大模型基础设施： 攻克 KV Cache 显存危机，异构集群如何承载 Agent 长程推理？ 可信 Agent 架构： 知识图谱 vs Long Context 记忆之争，设计防止死循环的业务"熔断按钮"。 MCP 协议实战： 解决接口调用"幻觉"与"误解"，实现 Agent 从对话到行动的精准对齐 如何看直播？ 扫描下图海报 【二维码】 或点击下方直播预约按钮，预约 AI 前线视频号直播。 可信 Agent 架构:知识图谱 vs ...

Investment Rating - The report maintains an investment rating of "Outperform the Market - A" [6]. Core Insights - The report highlights that AGI is progressing towards a stage of autonomous action, focusing on two main directions: Agent and embodied intelligence. The technology has evolved past the "perception-thought" application threshold and is moving towards "autonomous action" [16][18]. - The rapid iteration of models since 2023 has significantly enhanced the capabilities of Agent products in perception, planning, and memory. Key advancements include the transition of models from single text to multimodal capabilities, improved reasoning abilities, and a substantial reduction in model usage costs [23][29]. Summary by Sections 1. Technology Layer: Significant Evolution of Models and Tools - AGI is moving towards autonomous action, indicating a shift towards Agent and embodied intelligence [16]. - The key technologies have evolved, with a focus on enhancing reliability and standardization [19]. - The current phase is characterized as a transition from workflow to Agent, analogous to the rule-driven phase of autonomous driving [3][50]. 2. Industry Chain: Early Commercialization Models - The report identifies three main lines of evolution in the industry chain: the open-source vs. closed-source model debate, the competition among tech giants for potential value points, and the entry of small and medium enterprises into the tool layer [56]. - The competition between open-source and closed-source models is crucial for the commercialization capabilities of major model vendors [56][58]. - Major tech companies are actively entering the AI Agent space, focusing on leading reasoning models and various tool integrations [61]. 3. Investment Recommendations - The report suggests that the evolution of AI technology will benefit infrastructure for computing power, particularly in training vertical long-tail models and inference computing [11]. - It emphasizes the importance of hardware support for local deployment of Agents on devices like smartphones and PCs, which may lead to a replacement cycle [11]. - The report also highlights the need for personalized solutions in private deployment services, indicating a gap in current offerings [11].

Summary of Conference Call on AI and Multi-Agent Systems Industry Overview - The conference focuses on the AI industry, particularly the development of chatbots and multi-agent systems, highlighting the transition from single agents to multi-agent systems as a significant trend in AI technology [2][3][7]. Key Points and Arguments 1. **Current State of AI Agents**: The development of AI agents is limited by model capabilities and engineering challenges. Despite high expectations for agents that can replace humans in complex tasks, no mature products have emerged yet [3][4]. 2. **Minus Product**: The Minus product is not an innovative model but offers a new approach to achieving multi-tasking capabilities within existing model limitations. It has sparked interest in the industry for practical applications of agents [4][5]. 3. **Multi-Agent Systems (MAS)**: MAS is a crucial direction in AI development, where multiple agents collaborate to compensate for individual limitations. This system enhances task automation and has shown promising results post-Minuse product launch [5][15]. 4. **Technological Breakthroughs in 2024**: Key advancements in AI technology include improvements in perception, definition, memory, planning, and action, laying the groundwork for more sophisticated multi-agent systems [6][10]. 5. **Action Mechanisms**: Significant breakthroughs in the action phase include virtual machine forms that address data source access issues and agent orchestration capabilities that assign tasks to the most suitable agents [9][10]. 6. **Progress in Large Models**: Large models have made notable progress in reasoning and action through methods like Chain of Thought (COT) and Reasoning + Acting, although human intervention remains common in enterprise applications [10][11]. 7. **Code Agent Development**: Code agents have matured, capable of automating various coding tasks and expanding their application scenarios beyond just code generation [11][12]. 8. **Data Access and Personalization**: The extent of data access is a critical factor in extending general scenarios, with companies like Apple and Tencent working on integrating personal behavior data for enhanced services [12][13]. 9. **MCP Protocol**: The MCP protocol is designed for cloud systems to ensure standardized information sharing and task collaboration among agents, which is vital for the development of multi-agent systems [13][14]. 10. **Enterprise Demand for MAS**: Companies have complex task orchestration needs, leading to significant interest in multi-agent architectures. Firms like Workday, ServiceNow, and Salesforce are exploring these systems to maximize their value [28][30]. Additional Important Insights - **Future of Multi-Agent Technology**: Multi-agent technology is expected to evolve from individual agents to a network, becoming a vital part of the next generation of the internet. This technology will play an increasingly important role in consumer devices [29][30]. - **Open Source Frameworks**: Various open-source multi-agent frameworks are emerging, providing users with customizable solutions to meet their specific needs [25][27]. - **Coordination Mechanisms**: Multi-agent systems utilize both static and dynamic coordination mechanisms, with dynamic approaches becoming more prevalent in current applications [23][24]. This summary encapsulates the key discussions and insights from the conference call, emphasizing the current state and future potential of AI and multi-agent systems in the industry.