Beyond static responses: multi-agent LLM systems as a new paradigm for social science research

Abstract

Abstract As large language models (LLMs) transition from static tools to fully agentic systems, their potential for transforming social science research is well recognized. This paper introduces a structured framework for understanding the diverse applications of agentic LLM systems, ranging from simple data processors to complex, multi-agent systems capable of simulating emergent social dynamics. By mapping this developmental continuum across six levels, the paper clarifies the technical and methodological boundaries between different agentic architectures, surveying current capabilities and future potential. It highlights how lower-tier systems streamline conventional tasks like text classification and data annotation, while higher-tier systems enable new forms of inquiry, including the study of group dynamics, norm formation, and large-scale social processes. However, these advancements also raise challenges around reproducibility, ethical oversight, and emergent biases. The paper critically examines these concerns, arguing for sound validation methods, interdisciplinary collaboration, and standardized evaluation metrics. It argues that while agentic LLM systems offer considerable potential for the social sciences, using them responsibly will require careful, context-sensitive deployment and ongoing methodological refinement. The paper concludes with a call for future research that balances technical innovation with ethical responsibility, working toward agentic systems that not only replicate but also extend social science methodology.