The engineering design process often demands expertise from multiple domains, leading to complex collaborations and iterative refinements. Traditional methods can be resource-intensive and prone to inefficiencies. To address this, we formalize the engineering design process through a multi-agent AI framework that integrates structured design and review loops. The framework introduces specialized knowledge-driven agents that collaborate to generate and refine design candidates. As an exemplar, we demonstrate its application to the aerodynamic optimization of 4-digit NACA airfoils. The framework consists of three key AI agents: a Graph Ontologist, a Design Engineer, and a Systems Engineer. The Graph Ontologist employs a Large Language Model (LLM) to construct two domain-specific knowledge graphs from airfoil design literature. The Systems Engineer, informed by a human manager, formulates technical requirements that guide design generation and evaluation. The Design Engineer leverages the design knowledge graph and computational tools to propose candidate airfoils meeting these requirements. The Systems Engineer reviews and provides feedback both qualitative and quantitative using its own knowledge graph, forming an iterative feedback loop until a design is validated by the manager. The final design is then optimized to maximize performance metrics such as the lift-to-drag ratio. Overall, this work demonstrates how collaborative AI agents equipped with structured knowledge representations can enhance efficiency, consistency, and quality in the engineering design process.

翻译：工程设计过程通常需要多领域专业知识，导致复杂的协作与迭代优化。传统方法往往资源密集且易产生低效问题。为此，我们通过集成结构化设计与评审循环的多智能体AI框架，对工程设计过程进行形式化建模。该框架引入专门的知识驱动型智能体，通过协作生成并优化设计方案。作为范例，我们展示了其在4位数NACA翼型气动优化中的应用。该框架包含三个核心AI智能体：图谱本体构建师、设计工程师与系统工程师。图谱本体构建师采用大型语言模型（LLM）从翼型设计文献中构建两个领域专用知识图谱。系统工程师在人类管理者指导下制定技术需求，以指导设计生成与评估。设计工程师利用设计知识图谱与计算工具提出满足需求的候选翼型方案。系统工程师基于其自有知识图谱进行定性与定量评审并提供反馈，形成迭代反馈循环，直至设计方案通过管理者验证。最终设计将进一步优化以最大化升阻比等性能指标。总体而言，本研究展示了配备结构化知识表征的协作式AI智能体如何提升工程设计过程的效率、一致性与质量。