Generalist Generative Agent: Open-ended design exploration with large language models

Anton Savov, Angela, CheWei Lin, Benjamin Dillenburger

Abstract: Architects often navigate ambiguity in early-stage design by using metaphors and conceptual models to transform abstract ideas into architectural forms. However, current computational tools struggle with such exploratory processes due to narrowly defined design spaces. This paper investigates whether Large Language Models (LLMs) can offer an alternative generative paradigm by interpreting human intent and translating it into actionable design logic. We propose an Agentic AI framework in which LLM agents interpret metaphors, formulate design tasks, and generate procedural 3D models. Using this framework, we produced 1,000 procedural designs and 4,000 images based on 20 metaphors to demonstrate the emergent capabilities of LLMs for creating architecturally relevant conceptual models. Our findings suggest that LLMs effectively engage with ambiguity, delivering diverse, meaningful outputs with notable potential for early-phase design. We discuss the strengths and shortcomings of the AI agents within the framework and suggest ways to extend their capacity for tackling open-ended design challenges, thereby enhancing their relevance in architectural practice.