AI-Native Network Controller: A Modular Framework for Safe Agentic Control of Multi-Domain Network Infrastructure

• URL: http://arxiv.org/abs/2606.20565v1
• Date: Mon, 20 Apr 2026 21:09:30 GMT
• Title: AI-Native Network Controller: A Modular Framework for Safe Agentic Control of Multi-Domain Network Infrastructure
• Abstract summary: We present the AI-Native Network Controller (AI-NNC), an open-source and modular framework that enables agentic AI control across diverse network domains.<n>The framework is designed around a protocol-agnostic architecture in which each physical device is integrated through a lightweight Python adapter.
• Score: 0.4061135251278187
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The convergence of multiple network domains, including radio access, optical transport, and core networks, under unified intelligent control is a fundamental requirement for future 6G systems. This is important because existing network controllers remain largely domain-specific, such as the O-RAN RIC for radio, or they lack native support for AI-driven automation across heterogeneous infrastructure. As a result, safe and coordinated agentic control of multi-domain networks is still an open challenge. In this paper, we present the AI-Native Network Controller (AI-NNC), an open-source and modular framework that enables agentic AI control across diverse network domains. The framework is designed around a protocol-agnostic architecture in which each physical device is integrated through a lightweight Python adapter, while control logic is implemented through domain-specific control applications. Beyond closed-loop control, the framework also supports dataset collection, agentic AI experimentation, and coordinated testbed operation using the same validated control and measurement interfaces. This design enables a safer paradigm for autonomous network management, where AI agents operate through validated applications rather than issuing commands directly to network equipment.

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