An Agentic AI Control Plane for 6G Network Slice Orchestration, Monitoring, and Trading

TL;DR

This paper introduces an agentic AI control plane for 6G network slice management, integrating autonomous decision-making, market-awareness, natural language interaction, and explainability to enable scalable, adaptive, and economically optimized network operations.

Contribution

It proposes a layered architecture with multiple AI agents, market-aware orchestration, and a natural language interface, advancing beyond static 5G frameworks for dynamic 6G environments.

Findings

Demonstrated scalability and adaptability in a real-world testbed.

Enabled intent-based control through natural language interfaces.

Achieved closed-loop SLA assurance with agentic autonomy.

Abstract

6G networks are expected to be AI-native, intent-driven, and economically programmable, requiring fundamentally new approaches to network slice orchestration. Existing slicing frameworks, largely designed for 5G, rely on static policies and manual workflows and are ill-suited for the dynamic, multi-domain, and service-centric nature of emerging 6G environments. In this paper, we propose an agentic AI control plane architecture for 6G network slice orchestration, monitoring, and trading that treats orchestration as a holistic control function encompassing slice planning, deployment, continuous monitoring, and economically informed decision-making. The proposed control plane is realized as a layered architecture in which multiple cooperating AI agents. To support flexible and on-demand slice utilization, the control plane incorporates market-aware orchestration capabilities, allowing slice requirements, pricing, and availability to be jointly considered during orchestration decisions. A natural language interface, implemented using the Model Context Protocol (MCP), enables users and applications to interact with control-plane functions through intent-based queries while enforcing safety and policy constraints. To ensure responsible and explainable autonomy, the control plane integrates fine-tuned large language models organized as a multi-model consortium, governed by a dedicated reasoning model. The proposed approach is evaluated using a real-world testbed with multiple mobile core instances (e.g Open5GS) integrated with Ericsson's RAN infrastructure. The results demonstrate that combining agentic autonomy, closed-loop SLA assurance, market-aware orchestration, and natural language control enables a scalable and adaptive 6G-native control plane for network slice management, highlighting the potential of agentic AI as a foundational mechanism for future 6G networks.