Aethon: A Reference-Based Replication Primitive for Constant-Time Instantiation of Stateful AI Agents
For AI infrastructure engineers, Aethon addresses the scalability bottleneck of stateful agent instantiation, offering a more efficient systems abstraction for production-scale multi-agent orchestration.
Aethon introduces a reference-based replication primitive for near-constant-time instantiation of stateful AI agents, reducing latency and memory overhead compared to materialization-heavy models. The approach decouples creation cost from inherited structure, enabling lightweight, composable agent spawning.
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.