Agent Framework

Agent frameworks leverage large language models (LLMs) to create autonomous agents capable of performing complex tasks, often involving interaction with external tools and environments. Current research emphasizes improving agent capabilities through multi-agent systems, enhanced memory and reasoning mechanisms (e.g., incorporating global workspaces or layered memory), and the use of evolutionary algorithms for automated agent design. This field is significant for advancing AI capabilities in diverse areas, from software development and scientific data visualization to simulating complex real-world scenarios like legislative processes and urban mobility, ultimately leading to more efficient and effective systems.