A Survey on the Application of Agentic AI in Gaming

Ling Xu

doi:10.54254/2755-2721/2025.LD27634

Applied and Computational EngineeringOpen access

A Survey on the Application of Agentic AI in Gaming

Research Article

Open Access

A Survey on the Application of Agentic AI in Gaming

Ling Xu ^1*

¹ Institute of Physics, Truman State University, 100 E. Normal Avenue, Kirksville, USA

^*Corresponding author: xu62284@truman.edu

Published on 14 October 2025

ACE Vol.191

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-184-6

ISBN (Online): 978-1-80590-129-7

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Abstract

Since the release of Practices for Governing Agentic AI Systems by OpenAI in 2023, the emerging paradigm of Agentic AI (Artificial Intelligence) has gradually attracted academic attention. Unlike traditional AI systems that rely on structured presets and extensive human intervention, Agentic AI refers to intelligent systems capable of understanding task objectives, adapting to complex environments, and autonomously completing tasks with minimal human oversight. Its advantages in adaptability, decision-making, and self-management make it particularly suited to dynamic and rapidly changing real-world scenarios. This paper first outlines the definition, core characteristics, and primary enabling technologies of Agentic AI, emphasizing that, to date, no deployed system in the gaming domain fully embodies all of its defining features. To explore potential pathways toward practical implementation, the paper analyzes several recent large-model-based agents—such as SIMA, and ChatRPG v2, that, while falling short of the full Agentic AI standard, exhibit partial alignment through capabilities such as autonomous instruction comprehension, long-term task execution, multi-environment adaptation, and low-intervention deployment. These systems can therefore be regarded as generative agents with Agentic characteristics, offering valuable insights for future research directions and technical architectures. Finally, the paper proposes using gaming environments as low-cost, low-risk, and highly controllable testbeds for validating key capabilities, accumulating deployment experience, and accelerating the transition of Agentic AI into real-world applications, thereby advancing game AI from scripted logic toward higher levels of autonomous intelligence.

Keywords:

Agentic AI, Agentic gaming, Multi-Agent Systems, Reinforcement Learing, Multimodal Perception and Interation

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