Defining AI-Native Systems: Autonomy as Revision Authority

Cheng Tan

Defining AI-Native Systems: Autonomy as Revision Authority

Artificial Intelligence 2026-07-22 v1 Operating Systems

Authors: Cheng Tan

Abstract

AI has begun to write systems code: agents now synthesize, verify, and deploy system components. Despite this shift, "AI-native" remains a marketing term with no precise technical definition. This paper gives it one. We define AI-nativeness along a single axis---authority over the system's own decisions rather than by the capability of the underlying AI models. Building on a decision-level model of a system, we distinguish occupancy (who executes a decision) from revision authority (who may change it), organize revision authority into a ladder---self-tuning, self-rewriting, self-architecting and define a system as AI-native when an AI autonomously rewrites the system's own implementations. The definition further requires an escalation detector, a verification procedure, and a verified fallback, while leaving purpose and correctness human-owned.

Cite

@article{arxiv.2607.21659,
  title  = {Defining AI-Native Systems: Autonomy as Revision Authority},
  author = {Cheng Tan},
  journal= {arXiv preprint arXiv:2607.21659},
  year   = {2026}
}