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This paper establishes a rigorous technical definition of "AI-native" systems, shifting the focus from model capabilities to the AI agent's authority over its own code revisions and architecture. By introducing concepts like revision authority ladders (self-tuning through self-architecting) alongside mandatory safety mechanisms such as escalation detectors and verified fallbacks, it provides a framework for safely transitioning toward autonomous system evolution while retaining human oversight on purpose and correctness.
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Defining AI-Native Systems: Autonomy as Revision Authority

arXiv2 min read

arXiv:2607.21659v1 Announce Type: new 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.

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.

Artificial Intelligence (cs.AI); Operating Systems (cs.OS)

arXiv:2607.21659 [cs.AI]

(or arXiv:2607.21659v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.21659 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 217 words

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