Abstract
—The open radio access network (O-RAN) exposes rich control and telemetry interfaces across the non-real-time RAN intelligent controller (Non-RT RIC), near-real-time RIC (Near-RT RIC), and distributed units, but also complicates the operation of multi-tenant, multi-objective RANs in a safe and auditable manner. In parallel, agentic artificial intelligence (AI) systems with explicit planning, tool use, memory, and self-management offer a natural way to structure long-lived control loops. This article studies how such agentic controllers can be brought into O-RAN. We contrast agentic controllers with conventional machine learning (ML)/reinforcement learning (RL) xApps and organize the O-RAN task landscape around three clusters: network slice life-cycle, radio resource management (RRM) closed loops, and cross-cutting security, privacy, and compliance. We then introduce a compact set of agentic primitives —Plan-Act-Observe-Reflect, skills as tool use, memory and evidence, and self-management gates— and show, in a multi-cell O-RAN simulation, that they improve slice life-cycle and RRM performance relative to conventional baselines and ablations that remove individual primitives. The framework achieves an average 8.83% reduction in resource usage across three classic network slices.