Defining AI-Native Systems: Autonomy as Revision Authority

2026-07-22

AI Summary

This work addresses the current ambiguity surrounding the term “AI-native systems,” which is often used loosely as a marketing slogan rather than a rigorously defined concept. Building upon a decision hierarchy model, the study proposes a novel technical definition centered on a system’s capacity for autonomous self-rewriting—specifically, its ability to revise its own implementation. The authors introduce “revision authority” as the core criterion and delineate a capability ladder ranging from self-tuning to self-architecting. To operationalize this framework, they integrate an upgrade detector, formal verification mechanisms, and empirically validated fallback strategies. This approach establishes a concrete, actionable foundation for both evaluating and engineering genuinely autonomous AI systems, offering both theoretical clarity and practical engineering guidance.

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.

Problem

• AI-native
• autonomy
• revision authority
• system definition
• decision authority

Innovation

• AI-native systems
• revision authority
• autonomous rewriting
• decision-level model
• verified fallback