On the Creativity of AI Agents

arXiv cs.AI / 4/16/2026

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Key Points

The paper examines whether LLM-based agentic systems can be considered “creative,” arguing that the answer depends on how creativity is defined and evaluated.
It analyzes creativity using two macro perspectives: functionalist views based on observable creative outputs, and ontological views focused on underlying processes plus social and personal dimensions.
The authors contend that LLM agents show functionalist creativity at intermediate levels, but still miss key elements required for fully ontological creativity.
The paper weighs the desirability of agents achieving both forms of creativity by considering potential benefits and risks, and it outlines pathways toward “artificial creativity” aligned with human societal goals.

Abstract

Large language models (LLMs), particularly when integrated into agentic systems, have demonstrated human- and even superhuman-level performance across multiple domains. Whether these systems can truly be considered creative, however, remains a matter of debate, as conclusions heavily depend on the definitions, evaluation methods, and specific use cases employed. In this paper, we analyse creativity along two complementary macro-level perspectives. The first is a functionalist perspective, focusing on the observable characteristics of creative outputs. The second is an ontological perspective, emphasising the underlying processes, as well as the social and personal dimensions involved in creativity. We focus on LLM agents and we argue that they exhibit functionalist creativity, albeit not at its most sophisticated levels, while they continue to lack key aspects of ontological creativity. Finally, we discuss whether it is desirable for agentic systems to attain both forms of creativity, evaluating potential benefits and risks, and proposing pathways toward artificial creativity that can enhance human society.