Building on AGP, we present Autogenesis System (AGS), a self-evolving multi-agent system that dynamically instantiates, retrieves, and refines protocol-registered resources during execution.
大多数人认为多智能体系统应该在设计阶段就确定各个智能体的角色和交互方式，而不是在执行过程中动态调整。但作者提出的AGS系统强调在运行时动态实例化、检索和细化协议注册的资源，这挑战了传统多智能体系统的设计范式，引入了一种更加灵活和动态的智能体协作方式。
While the initial system has been set up correctly, data systems are never static and as a result the context layer shouldn't be either. Data sources and formats can change upstream and individuals may have custom instructions they'll want to add and modify based on changing business requirements.
这是一个关于上下文层动态性质的深刻见解。文章强调了上下文层不应是静态的，而应随着数据系统和业务需求的变化而不断更新。这挑战了传统数据仓库的静态模型，提出了一个更加灵活和自适应的AI系统架构。
Della Rossa, F., & DeLellis, P. (2020). Stochastic master stability function for noisy complex networks. Physical Review E, 101(5), 052211. https://doi.org/10.1103/PhysRevE.101.052211