iDesignGPT enhances conceptual design via large language model agentic workflows

Conceptual engineering system design faces challenges from traditional methods and emerging AI tools to fully address its inherently complex, dynamic, and creativity-driven demands. iDesignGPT is a framework that integrates large language models with established design methodologies to enable dynamic multi-agent collaboration for problem refinement, information gathering, design space exploration, and evaluation. By incorporating design metrics such as coverage, diversity, and novelty, iDesignGPT provides quantitative insights for early-stage conceptual design. Performance evaluations across six public design challenges show that iDesignGPT achieves competitive novelty and consistently higher originality and modularity than GPT-4o zero-shot, GPT-4o chain-of-thought and Deepseek-r1, based on metrics and expert assessments. Two controlled user studies show positive reception across profiles and, for novice designers, lower mental demand than human-only design and clearer design flow with iDesignGPT. These results establish iDesignGPT as a practical framework for integrating language-model agents with established engineering design methods, enabling metrics-driven support for conceptual design by both expert and novice designers. Conceptual engineering design is challenged by its inherent complexity, dynamics, and creativity demands. Here, the authors integrate large language models with established design methodologies in a multiagent framework for early-stage conceptual design.