Critical domain knowledge typically resides with few experts, creating organizational bottlenecks in scalability and decision-making. Non-experts struggle to create effective visualizations, leading to suboptimal insights and diverting expert time. This paper investigates how to capture and embed human domain knowledge into AI agent systems through an industrial case study. We propose a software engineering framework to capture human domain knowledge for engineering AI agents in simulation data visualization by augmenting a Large Language Model (LLM) with a request classifier, Retrieval-Augmented Generation (RAG) system for code generation, codified expert rules, and visualization design principles unified in an agent demonstrating autonomous, reactive, proactive, and social behavior. Evaluation across five scenarios spanning multiple engineering domains with 12 evaluators demonstrates 206% improvement in output quality, with our agent achieving expert-level ratings in all cases versus baseline's poor performance, while maintaining superior code quality with lower variance. Our contributions are: an automated agent-based system for visualization generation and a validated framework for systematically capturing human domain knowledge and codifying tacit expert knowledge into AI agents, demonstrating that non-experts can achieve expert-level outcomes in specialized domains.

翻译：关键领域知识通常仅掌握在少数专家手中，这造成了组织在可扩展性和决策制定方面的瓶颈。非专业人员难以创建有效的可视化图表，导致洞察力不足并挤占专家时间。本文通过工业案例研究探讨如何将人类领域知识捕获并嵌入AI智能体系统。我们提出一个软件工程框架，通过为大型语言模型（LLM）增强请求分类器、用于代码生成的检索增强生成（RAG）系统、编码化的专家规则以及统一于智能体中的可视化设计原则，在仿真数据可视化领域构建能够展示自主、反应式、主动式及社交行为的AI智能体。在跨越多个工程领域的五个场景中，由12名评估者进行的实验表明：输出质量提升206%，我们的智能体在所有案例中均获得专家级评分（基线表现较差），同时保持更优的代码质量且方差更低。我们的贡献包括：一个基于智能体的自动化可视化生成系统，以及一个经过验证的、用于系统化捕获人类领域知识并将隐性专家知识编码至AI智能体的框架，该研究证明非专业人员能够在专业领域实现专家级成果。