Leveraging large language models (LLMs), autonomous agents have significantly
improved, gaining the ability to handle a variety of tasks. In open-ended
settings, optimizing collaboration for efficiency and effectiveness demands
flexible adjustments. Despite this, current research mainly emphasizes fixed,
task-oriented workflows and overlooks agent-centric organizational structures.
Drawing inspiration from human organizational behavior, we introduce a
self-organizing agent system (S-Agents) with a "tree of agents" structure for
dynamic workflow, an "hourglass agent architecture" for balancing information
priorities, and a "non-obstructive collaboration" method to allow asynchronous
task execution among agents. This structure can autonomously coordinate a group
of agents, efficiently addressing the challenges of an open and dynamic
environment without human intervention. Our experiments demonstrate that
S-Agents proficiently execute collaborative building tasks and resource
collection in the Minecraft environment, validating their effectiveness.

利用大型语言模型（LLMs），自主代理在处理各种任务方面取得了显著改进。我们引入了一种基于树状代理结构的自组织代理系统（S-Agents），用于动态工作流程，采用 “沙漏代理结构” 的方式来平衡信息优先级，并通过 “非阻碍式协作” 方法实现代理之间的异步任务执行。我们的实验证明 S-Agent 能够在 Minecraft 环境中高效执行合作建筑任务和资源收集任务，并验证了它们的有效性。