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arXiv AI By Seyed Bagher Hashemi Natanzi, Bo Tang

Taming the Agentic RAN: Stability-Guaranteed Arbitration of Autonomous AI Agents in O-RAN

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The paper demonstrates that autonomous AI agents (rApps) in an O-RAN control plane can independently close control loops over shared radio resources, leading to unsafe interactions when multiple agents pursue different objectives. The authors introduce AURA, a lightweight arbitration layer that enforces feasibility invariants, dwell times, and a deadband to ensure stable operation. Implemented on an OpenAirInterface testbed, AURA reduces shared-state excursions by more than an order of magnitude and virtually eliminates cross-slice throughput starvation while maintaining latency compliance for the protected slice.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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