The transition from stateless model inference to stateful agentic execution is reshaping the systems assumptions underlying modern AI infrastructure. While large language models have made persistent, tool-using, and collaborative agents technically viable, existing runtime architectures remain constrained by materialization-heavy instantiation models that impose significant latency and memory overhead. This paper introduces Aethon, a reference-based replication primitive for near-constant-time instantiation of stateful AI agents. Rather than reconstructing agents as fully materialized objects, Aethon represents each instance as a compositional view over stable definitions, layered memory, and local contextual overlays. By shifting instantiation from duplication to reference, Aethon decouples creation cost from inherited structure. We present the conceptual framework, system architecture, and memory model underlying Aethon, including layered inheritance and copy-on-write semantics. We analyze its implications for complexity, scalability, multi-agent orchestration, and enterprise governance. We argue that reference-based instantiation is not merely an optimization, but a more appropriate systems abstraction for production-scale agentic software. Aethon points toward a new class of AI infrastructure in which agents become lightweight, composable execution identities that can be spawned, specialized, and governed at scale.

翻译：从无状态模型推理到有状态智能体执行的转变正在重塑现代AI基础设施的系统假设。尽管大语言模型已使持久、工具使用和协作式智能体在技术上可行，但现有运行时架构仍受限于以具体化为主的实例化模型，这些模型会带来显著的延迟和内存开销。本文提出Aethon，一种基于引用的复制原语，用于有状态AI智能体的近常数时间实例化。Aethon不将智能体重构为完全具体化的对象，而是将每个实例表示为稳定定义、分层记忆和局部上下文叠加的组合视图。通过将实例化从复制转变为引用，Aethon将创建成本与继承结构解耦。我们介绍了Aethon的概念框架、系统架构和内存模型，包括分层继承和写时复制语义，并分析了其对复杂性、可扩展性、多智能体编排以及企业治理的影响。我们认为，基于引用的实例化不仅是一种优化，更是面向生产级智能体软件的更合适的系统抽象。Aethon指向了一类新型AI基础设施，其中智能体成为轻量级、可组合的执行身份，能够以大规模方式进行生成、特化与管理。