Enhancing Game AI Behaviors with Large Language Models and Agentic AI

Integrating advanced AI behaviors is central to creating immersive and dynamic video game experiences. This paper presents a novel approach to improving AI behaviors in games using Large Language Models (LLMs) and agent-based AI. By orchestrating various interconnected parts, we propose a framework that facilitates the creation of complex behavior trees (BTs) for non-player characters (NPCs). Our method bridges the gap between source code and visual tools in game engines and enables both technical and non-technical stakeholders to effectively contribute to the development process. We also aim to increase the diversity of observable behaviors and testability of games through the same methods. The proposed architecture is designed to be adaptable to different game engines to ensure scalability and flexibility. In a collaboration between industry and academia, we validate our approach and demonstrate its potential to improve game AI development and make it more accessible and efficient. To promote the adoption of the methods, we consider small-sized models that run on typical developer platforms without the need for external solutions or expensive computing resources.