Agents for Experiments, Experiments for Agents: A Design Grammar for AI-Enabled Experimental Science

Abstract

Human-AI experiments vary who receives information, who can act, and who holds decision authority. Precise descriptions of individual studies do not by themselves provide a common language for comparing these arrangements. This conceptual commentary proposes SEED (Structural Encoding for Experimental Discovery), a grammar that represents each experimental condition as a typed actor-flow graph. Its purpose is to organize publicly documented studies into a shared library of consistently represented conditions. Human researchers and AI systems can use this common reference to understand prior arrangements and propose extensions for new research questions. The grammar distinguishes structural changes from attribute variation; the scientific value of a proposed change requires substantive argument and evidence. Computational illustrations demonstrate how SEED can support design comparison and AI-assisted development of candidate experimental conditions. We discuss implications for future replication and provenance work, including under restricted data access, and risks of selective search. We call for community agreement on the grammar and coordinated construction and maintenance of the library.