An LLM-based multi-agent system to assist early-stage product design and evaluation

Conceptual design decisions critically influence product performance, cost, and sustainability, yet integrating rigorous feasibility evaluation early in this creative phase remains challenging. While generative AI accelerates concept generation, current methods often lack mechanisms to assess the feasibility of proposed designs. To bridge this gap, this paper presents DesignAgent, an LLM-based multi-agent system that assists early-stage product design and evaluation. The system features specialised agents that collaborate to interpret requirements, produce 3D prototypes, and automatically evaluate feasibility through integrated finite element analysis. This agent-driven framework facilitates an automated, iterative design loop where simulation feedback informs concept refinement. We evaluated the system through a case study involving 104 simulated design sessions for three types of UAV landing gear. A comprehensive assessment involving human expert review, AI (LLM) evaluation, and quantitative analysis demonstrates the high proficiency of the system. The system achieved over 90% accuracy in core tasks, effectively utilised FEA feedback with an 84.3% meaningful refinement rate, and also showed excellent adherence to engineering constraints and effective parameter refinement towards improved design quality. A user study further showed that DesignAgent achieved higher design accuracy and solution quality, lower workload, and better human–AI collaboration than the baseline systems.