本文聚焦于基于大模型的 AI 智能体的构建及其创新场景应用研究。通过梳理国内外研究现状，呈现 OpenAI、Google、DeepMind 等国外机构与清华、北大等国内高校及企业在该领域的前沿探索成果。详细剖析 AI 智能体基础理论，包括其定义、特性、分类，以及大模型技术两者融合机制。深入探讨智能体构建技术，涵盖构建框架、关键组件、工具集成及多智能体系统构建。结合医疗、金融、教育、制造业等行业实际案例，深度解析智能体应用模式与价值，并对智能家居、娱乐等新兴场景进行展望。最后，识别研究面临的挑战，提出针对性解决方案与未来发展方向，为推动基于大模型的 AI 智能体技术进步与广泛应用提供理论与实践参考。

This paper focuses on the construction of large language model (LLM)-based AI agents and their innovative application scenarios. By reviewing current research progress globally, it showcases cutting-edge explorations by international institutions (e.g., OpenAI, Google, DeepMind) and domestic organizations (e.g., Tsinghua University, Peking University, and Chinese enterprises) in this field. The study systematically analyzes fundamental theories of AI agents, including definitions, characteristics, classification criteria, and their integration mechanisms with LLM technology. It thoroughly investigates agent construction methodologies, covering architectural frameworks, core components, tool integration strategies, and multi-agent system development. Through practical case studies across healthcare, finance, education, and manufacturing industries, the paper conducts in-depth analysis of agent application patterns and value propositions, while exploring prospects in emerging scenarios like smart homes and entertainment. Finally, it identifies current research challenges, proposes targeted solutions and future development directions, aiming to provide theoretical and practical references for advancing LLM-based AI agent technologies and their widespread adoption.