Artificial General Intelligence (AGI), widely regarded as the fundamental goal of artificial intelligence, represents the realization of cognitive capabilities that enable the handling of general tasks with human-like proficiency. Researchers in brain-inspired AI seek inspiration from the operational mechanisms of the human brain, aiming to replicate its functional rules in intelligent models. Moreover, with the rapid development of large-scale models in recent years, the concept of agents has garnered increasing attention, with researchers widely recognizing it as a necessary pathway toward achieving AGI. In this article, we propose the concept of a brain-inspired AI agent and analyze how to extract relatively feasible and agent-compatible cortical region functionalities and their associated functional connectivity networks from the complex mechanisms of the human brain. Implementing these structures within an agent enables it to achieve basic cognitive intelligence akin to human capabilities. Finally, we explore the limitations and challenges for realizing brain-inspired agents and discuss their future development.

翻译：通用人工智能（AGI）被广泛视为人工智能的根本目标，其代表着认知能力的实现，使系统能够以类人熟练度处理通用任务。脑启发AI研究者从人脑运行机制中寻求灵感，旨在将大脑的功能规则复现于智能模型中。此外，随着近年来大模型的快速发展，智能体的概念日益受到关注，学界普遍将其视为实现AGI的必经路径。本文提出脑启发AI智能体的概念，分析如何从复杂的人脑机制中提取相对可行且与智能体兼容的皮层区域功能及其关联的功能连接网络。在智能体中实现这些结构可使其获得类人的基础认知智能。最后，我们探讨实现脑启发智能体的局限性与挑战，并展望其未来发展。