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从App到Agent,亚马逊云科技助推的软件范式跃迁
Sou Hu Cai Jing· 2025-12-11 06:13
Core Insights - The emergence of AI Agents is seen as a pivotal moment in AI development, transforming various industries and altering work, life, and learning practices [2][11] - Software is shifting from a process and function-centric model to one focused on capabilities and execution, marking a transition from App to Agent models [2][11] Group 1: AI Agents and Their Impact - AI Agents possess capabilities such as perception, understanding, planning, action, and self-feedback, enabling them to autonomously complete tasks without relying on manual instructions [4] - The successful deployment of Agents requires four core elements: AI infrastructure, reasoning systems, enterprise data, and Agent construction tools [4][6] - Amazon Web Services (AWS) introduced the Amazon AI Factory service, allowing AI infrastructure to be deployed in customer data centers, providing a similar experience to public cloud without data upload concerns [4] Group 2: Amazon Bedrock AgentCore - Amazon Bedrock AgentCore is a new platform that enables enterprises to scale, securely build, deploy, and operate Agents, significantly reducing the time from proof of concept to production [6] - The platform features a modular design with seven delivered components, enhancing the ease and speed of Agent construction [6][7] - AWS emphasizes addressing challenges in Agent deployment, such as security and management, through features like AgentCore Policy and AgentCore Evaluations [7] Group 3: Development Tools and Paradigm Shift - The introduction of Kiro, a platform for building and managing Agents, allows for automation in task execution and analysis, transforming traditional software development practices [9][10] - Kiro Autonomous Agent acts as a virtual developer, automating various tasks and learning from team interactions, while Amazon Security Agent functions as a virtual security engineer [10] - The transition to Agent-based software development signifies a shift from application-centric to task-centric approaches, leading to lower development costs and faster delivery [11]