【摘要】 针对通用大语言模型AI智能体的设计方法缺乏系统构建，智能体的设计流程与内容生成设计机制缺乏设计本体论方法指导等问题，构建一种面向垂直领域的大语言模型AI智能体的设计方法。搭建面向垂直领域的多源数据处理体系，构建高质量的语义向量表示；设计混合检索架构，提升检索的相关性和覆盖率；引入链式思维与多路径推理机制，结合提示优化策略，提高生成回答的准确性、可控性与一致性；最终提升垂域大模型AI智能体感知认知、规划决策、执行作业与交互进化能力。以智慧城市系统为例，构建了集城市动态感知、灾害预警推演、城市智能解读、民生智能问答于一体的大模型AI智能体系统。该研究验证了大模型AI智能体设计方法的可行性，为大模型AI智能体的理论研究与实践应用提供借鉴参考。更多还原

【Abstract】 To address the lack of systematic construction in the design methods of AI agents driven by the general-purpose Large Language Model(LLM), and the absence of design ontology guidance for their design workflows and content generation mechanisms, the work aims to establish a design methodology for LLM-driven AI agents in vertical domains. A multi-source data processing framework for vertical domains is established to construct high-quality semantic vector representations. A hybrid retrieval architecture is designed to enhance retrieval relevance and coverage. By incorporating chain-of-thought and multi-path reasoning mechanisms combined with prompt optimization strategies, the accuracy, controllability, and consistency of generated responses are improved. Ultimately, this approach enhances the perception-cognition, planning-decision making, execution, and interactive evolution capabilities of LLM-driven AI agents in vertical domain. Using a Smart City system as a case study, we constructed a platform integrating urban dynamic perception, disaster warning simulation, intelligent urban analytics, and citizen service Q&A.. This study validates the feasibility of the design methodology, providing theoretical and practical insights for the research and application of LLM-driven AI agents.更多还原