Taming the Agentic RAN: Stability-Guaranteed Arbitration of Autonomous AI Agents in O-RAN
This work demonstrates on a live O-RAN testbed that two autonomous agents with individually correct objectives—one protecting a latency SLA and one maximizing utilization for energy efficiency—jointly drive recurring opposing excursions of the shared resource partition, and presents and proves a lightweight arbitration layer, AURA, whose three admission checks (feasibility invariants, per-variable dwell time, deadband) reduce shared-state excursion amplitude from 8.4 to 0.4 PRB and cross-slice throughput starvation from 40–55% to 0.3%, while leaving the protected slice's own latency compliance unchanged.