编辑部 整理自 凹非寺 量子位 | 公众号 QbitAI Agent的真正价值不在于演示效果多惊艳，而在于能否真正跑在生产环境里。 数据显示，超过93%的企业Agent项目卡在了从POC （概念验证）到生产的最后一公里。 在量子位MEET2026智能未来大会上， 亚马逊云科技大中华区产品部总经理陈晓建 系统阐述了企业级Agent从开发到生产的完整路径。 这个数字背后，是无数企业在Agent落地过程中踩过的坑：开发门槛高、工程化能力缺失、模型定制困难、安全边界模糊。 在刚刚结束的AWS re:Invent 2025大会上，亚马逊云科技将聚光灯对准了Agent。不是因为其他技术不重要，而是整个行业都意识到： Agent正在成为AI生产力释放的关键枢纽。 陈晓建的分享，正是围绕"如何让Agent真正跑起来"这一核心命题展开。 MEET2026智能未来大会是由量子位主办的行业峰会，近30位产业代表与会讨论。线下参会观众近1500人，线上直播观众350万+，获得了 主流媒体的广泛关注与报道。 核心观点梳理 一个成功的Agent需要三个模块：模型（大脑）、代码（逻辑）、工具（连接物理世界的手脚）。三者的有效连接是最大的工 ...

Core Insights - Tiger Global Management, led by Chase Coleman III, has significantly increased its investment in Amazon by purchasing over 4 million shares, making it the fund's fourth-largest holding [2] - Amazon's share price has become more reasonable, dropping from a high price-to-earnings ratio of 110x to 34x, while net income has surged by over 500% to $70.6 billion [6][7] - The company is experiencing strong growth, with net sales increasing by 13% year over year, driven by a 17.5% growth in Amazon Web Services (AWS) revenue [8] Investment Rationale - Amazon's financial struggles in 2022 led to a significant drop in share price, but the company has since rebounded with substantial income growth and a more attractive valuation [7] - AWS is crucial for Amazon's profitability, contributing over half of the company's profits despite accounting for only 18.4% of total revenue [9] - The ongoing expansion and innovation within AWS, including new partnerships and tools, suggest continued growth potential for the segment [10][11] Market Position - Amazon's valuation stands at $2.4 trillion, and its e-commerce segment still has room for growth, as it represents only 15.5% of total U.S. retail sales [8][9] - The company's ability to leverage its scale for innovation and efficiency positions it well for future success, making it an attractive investment opportunity for both institutional and individual investors [11]

Core Insights - The report outlines the practical guide for building Agentic AI applications, emphasizing its role as an autonomous software system based on large language models (LLMs) that can automate complex tasks through perception, reasoning, planning, and tool invocation [1][5]. Group 1: Agentic AI Technology Architecture and Key Technologies - Agentic AI has evolved from rule-based engines to goal-oriented architectures, with core capabilities including natural language understanding, autonomous planning, and tool integration [3][5]. - The technology architecture consists of single-agent systems for simple tasks and multi-agent systems for complex tasks, utilizing protocols for agent communication and tool integration [3][4]. Group 2: Building Solutions and Scenario Adaptation - Amazon Web Services offers three types of building solutions: dedicated agents for specific tasks, fully managed agent services, and completely self-built agents, allowing enterprises to choose based on their needs for task certainty and flexibility [1][4]. - The report highlights various application scenarios, such as optimizing ERP systems and automating document processing, showcasing the effectiveness of Agentic AI in reducing manual operations and improving response times [4][5]. Group 3: Industry Applications and Value Validation - Case studies include Kingdee International's ERP system optimization and Formula 1's root cause analysis acceleration, demonstrating the practical benefits of Agentic AI in different sectors [2][4]. - The manufacturing and financial sectors are also highlighted for their use of Agentic AI in automating contract processing and generating visual reports, respectively, which enhances decision-making efficiency [4][5]. Group 4: Future Trends and Challenges - The report discusses future trends indicating that Agentic AI will penetrate various fields, driven by advancements in model capabilities and standardized protocols [5]. - Challenges include ensuring the stability of planning capabilities, improving multi-agent collaboration efficiency, and addressing the "hallucination" problem in output credibility [4][5].

Core Insights - The article emphasizes that multi-agent AI represents the next significant direction for large models, showcasing unprecedented capabilities and indicating a major iteration in large language models (LLMs) [1][3][9] - Amazon Web Services (AWS) is leading the charge with a comprehensive Agentic AI technology stack, facilitating the transition from concept to practical application [10][62] Group 1: Multi-Agent AI Developments - Recent releases like Grok 4 and Kimi K2 utilize multi-agent technology, enabling models to autonomously understand their task environment and utilize external tools to solve complex problems [2][4] - AWS's Agentic AI framework includes four pillars: model application capability, security and reliability, scalability, and deployment and production capability [5][6] - The introduction of Amazon Bedrock AgentCore allows for the construction and deployment of enterprise-level secure agent services through seven core services [14][17] Group 2: Agent Applications and Tools - The AgentCore Runtime provides a unique runtime environment for agent applications, supporting third-party models and significantly reducing deployment costs [20][21] - AWS has expanded its Amazon Bedrock platform to include 12 major model vendors, enhancing its capabilities in generative AI across various modalities [24][27] - The launch of Amazon S3 Vectors reduces vector storage and query costs by 90%, enabling agents to retain more context from interactions [50][52] Group 3: Collaboration and Development - The Strands Agents SDK has been upgraded to facilitate the creation of multi-agent systems, allowing for more efficient collaboration on complex tasks [38][39] - New protocols like Agent to Agent (A2A) enhance communication between agents, marking a shift towards proactive collaboration [41][46] - The introduction of various APIs and tools within Strands Agents V1.0 simplifies the development of multi-agent applications, lowering the barrier for developers [45][46] Group 4: Future Outlook - The article predicts that by 2025, agents will begin large-scale deployment, fundamentally changing how software interacts with the world and how humans interact with software [9][61] - AWS aims to create the most practical Agentic AI platform, supporting companies of all sizes in deploying reliable and secure agent solutions [62][63] - The ongoing evolution of agent technology is expected to lead to more disruptive applications, enhancing the integration of AI as a digital colleague in business operations [64][65]

Core Insights - AWS is preparing a comprehensive upgrade of its AI cloud platform "Bedrock" due to customer attrition and competition from Google Cloud and Microsoft Azure [1][6] - The upgrade aims to enhance flexibility and usability for building AI agents, which are seen as a significant growth opportunity for AWS [2][6] Group 1: Current Challenges - AWS is facing pressure as customers find Google Cloud and Microsoft Azure easier to use for developing AI applications, particularly with open-source software [2][3] - Bedrock currently has limited model selection and lacks compatibility with OpenAI models, making it cumbersome for customers to operate across multiple cloud platforms [2][3] - Some customers have turned to alternative open-source tools from startups like Hugging Face and PydanticAI, bypassing AWS for their AI development needs [3] Group 2: Upgrade Plans - The upcoming Bedrock upgrade will focus on making the platform more open and flexible, allowing easier access to various AI models and development tools [4][5] - AWS has also released a set of open-source development tools called Strands Agents to facilitate the creation of AI agents using text prompts [4] Group 3: Competitive Landscape - The upgrade is part of a broader competitive landscape where AI applications are critical for cloud service providers due to their high profit margins [6] - AWS aims to retain its enterprise customers and prevent them from migrating to competitors like Microsoft and Google, which have seen faster revenue growth in AI [6]