Hierarchical Agent

Hierarchical agents represent a powerful approach to building complex AI systems by decomposing tasks into nested sub-tasks managed by specialized agents. Current research focuses on leveraging large language models (LLMs) within these hierarchies for high-level planning and reasoning, coupled with lower-level agents for execution and control, often employing reinforcement learning or imitation learning for training. This approach improves efficiency, generalizability, and interpretability compared to monolithic agent designs, with applications ranging from autonomous driving and robotic manipulation to optimizing large-scale API calls and power grid management.