An Agentic AI Control Plane for 6G Network Slice Orchestration, Monitoring, and Trading
Eranga Bandara, Ross Gore, Sachin Shetty, Ravi Mukkamala, Tharaka Hewa, Abdul Rahman, Xueping Liang, Safdar H. Bouk, Amin Hass, Peter Foytik, Ng Wee Keong, Kasun De Zoysa

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…
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Taxonomy
TopicsSoftware-Defined Networks and 5G · IoT and Edge/Fog Computing · Software System Performance and Reliability
