AI 中文总结
智能体人工智能系统处于发展试验阶段，缺乏核心抽象概念共识。研究提出借鉴经典和云操作系统发展路径，扩展原语推导新智能体抽象概念，精确指定语义并整合，以推动该领域发展。
AI 中文摘要
每一波主要的平台软件发展都遵循相同轨迹：初期对竞争框架和临时实现进行试验，接着明确一小组具有明确定义语义的稳定抽象概念，最后围绕这些抽象概念整合为应用可移植的平台。POSIX为经典操作系统如此，Kubernetes为云操作系统如此。智能体人工智能系统正处于第三波发展的试验阶段，虽涌现众多框架和协议，但核心抽象概念及保障尚无社区共识。这导致应用无法便携编写、平台无法可靠组合、领域难以超越原型部署。我们认为应借鉴前一波方法：通过扩展经典操作系统和云操作系统原语到随机、自然语言介导的执行来推导新的智能体抽象概念，精确指定其语义并围绕它们整合。
英文摘要
Every major wave of platform software follows the same arc: an initial period of experimentation with competing frameworks and ad-hoc implementations, followed by the articulation of a small set of stable abstractions with well-defined semantics, and finally consolidation around those abstractions into a platform that applications can portably target. POSIX did this for classical operating systems; Kubernetes did it for the cloud. Agentic AI systems - autonomous, LLM-driven agents that plan, use tools, maintain memory, and collaborate - are currently in the experimentation phase of the third such wave. dozens of frameworks and protocols have emerged, but no community consensus exists on what the core abstractions are or what guarantees they carry. Without that consensus, agentic applications cannot be written portably, platforms cannot compose reliably, and the field cannot advance beyond prototype deployments. We argue that the path forward is to follow the prior-wave methodology: derive new agentic abstractions by extending classical OS and cloud OS primitives to stochastic, natural-language-mediated execution, specify their semantics precisely, and consolidate around them - just as POSIX and Kubernetes consolidated their respective waves.
