Conference Agenda

Recent advances in large language models (LLMs) have created new opportunities for supporting engineering innovation and inventive problem solving. However, current AI-based ideation systems still exhibit important limitations, including weak integration between creative generation, prior-art analysis, novelty assessment, and iterative refinement within unified engineering-design workflows. This study presents Avolution, a retrieval-grounded multi-agent framework designed to support systematic innovation through the integration of TRIZ-inspired reasoning, engineering-design methodologies, retrieval-augmented generation, and generative AI. The proposed architecture coordinates specialized agents dedicated to problem reformulation, prior-art retrieval, inventive-trigger generation, exploratory concept generation, and design-around refinement. The workflow was implemented as a modular orchestration pipeline on the n8n platform, integrating patent repositories, scientific databases, web retrieval systems, and multimodal generative modules within a unified human-in-the-loop environment. Experimental validation was conducted across heterogeneous engineering-design scenarios involving compliant mechanisms, energy harvesting systems, and safety-critical transportation problems. Compared with unconstrained monolithic LLM ideation, the proposed framework expanded the explored solution space, improved technical grounding and prior-art awareness, and generated more contextually relevant and structurally differentiated engineering concepts. The results suggest that orchestrated retrieval-grounded multi-agent reasoning may represent a promising direction for supporting systematic engineering innovation beyond conventional generative-AI interaction paradigms.