Aethon: A Reference-Based Replication Primitive for Constant-Time Instantiation of Stateful AI Agents
Swanand Rao, Kiran Kashalkar, Parvathi Somashekar, Priya Krishnan

TL;DR
Aethon introduces a reference-based instantiation method for stateful AI agents, enabling near-instant creation by avoiding full materialization, thus improving scalability and efficiency in AI infrastructure.
Contribution
It proposes a novel reference-based approach for fast, scalable instantiation of AI agents, shifting from duplication to referencing for improved performance.
Findings
Aethon achieves near-constant-time agent instantiation.
The system reduces memory overhead compared to traditional models.
It enables scalable multi-agent orchestration and governance.
Abstract
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…
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