Recent advancements in large foundation models have remarkably enhanced our
understanding of sensory information in open-world environments. In leveraging
the power of foundation models, it is crucial for AI research to pivot away
from excessive reductionism and toward an emphasis on systems that function as
cohesive wholes. Specifically, we emphasize developing Agent AI -- an embodied
system that integrates large foundation models into agent actions. The emerging
field of Agent AI spans a wide range of existing embodied and agent-based
multimodal interactions, including robotics, gaming, and healthcare systems,
etc. In this paper, we propose a novel large action model to achieve embodied
intelligent behavior, the Agent Foundation Model. On top of this idea, we
discuss how agent AI exhibits remarkable capabilities across a variety of
domains and tasks, challenging our understanding of learning and cognition.
Furthermore, we discuss the potential of Agent AI from an interdisciplinary
perspective, underscoring AI cognition and consciousness within scientific
discourse. We believe that those discussions serve as a basis for future
research directions and encourage broader societal engagement.

利用大型基础模型提高我们对开放环境中感知信息的理解，在 AI 研究中强调发展集成大型基础模型的代理人 AI，这一新兴领域涉及机器人技术、游戏和医疗等多种具身和基于代理的多模态交互，并讨论了代理人 AI 在各个领域和任务中展现出的能力，挑战我们对学习和认知的理解，通过跨学科视角探讨了代理人 AI 的潜力，并强调将其纳入科学讨论中，为未来的研究方向提供基础并促进更广泛的社会参与。