World Models for Digital Agents: An Agent-Centric Survey

Digital agents operating in software-defined environments, including games, web interfaces, mobile GUIs, tool-use systems, and code repositories, often benefit from anticipating the consequences of actions before execution. World models provide a principled mechanism for such anticipation, but their utility in digital settings is neither automatic nor uniform. Unlike physical environments, digital transitions are interface-mediated and program-governed, and the value of prediction depends less on perceptual realism than on preserving functionally relevant consequences for downstream decisions. This survey presents the first dedicated agent-centric treatment of world models across major digital-agent domains. We introduce a unified design space, W = (X , L, U), organized around three complementary questions: what is modeled, how predictive capability is built, and how world models are used inside the agent loop. Through this lens, we synthesize a rapidly growing literature spanning semantic transition models, visual and latent simulators, tool-state predictors, executable code-based world models, and hybrid predictive systems. We further examine environments, datasets, and evaluation protocols as a coupled empirical pipeline, and highlight open challenges, notably the persistent gap between predictive fidelity and downstream utility.