Evaluating the Role of Model Size in Agentic AI for Expert-Like Material Selection

Material selection is fundamental to the design process, as it significantly affects the cost, performance, appearance, manufacturability, and sustainability of a product. It is a complex, open-ended challenge that forces designers to continuously adapt to new information, balance diverse stakeholder demands, weigh trade-offs, and navigate uncertainties to achieve the optimal outcome. Previous studies have explored the potential of large language models (LLMs) to assist in the material selection process, with findings suggesting that LLMs could provide valuable support. However, discrepancies between LLM outputs and expert recommendations indicate the need for further research. To address the limitations of standalone LLMs, particularly their lack of reasoning and action-execution capabilities, agentic AI has been developed with enhanced functionalities. These agents integrate LLMs with external search tools, allowing them to retrieve and analyze domain-specific information, iteratively refine responses, and improve decision-making alignment with experts. This study compares standalone LLMs and agentic AI frameworks, examining how search-augmented agents can more effectively emulate expert decision-making in material selection. Our findings reveal a nonlinear relationship between model size and performance, with some models demonstrating lower proximity to human survey results and struggling to follow instructions. These insights contribute to a broader understanding of AI integration in design workflows.