As 5G-Advanced evolves toward 6G, the Radio Access Network (RAN) is expected to become AI-native and intent-driven, delivering closed-loop autonomy under stringent operational resource and overhead constraints. Yet today’s RAN automation remains largely KPI-centric. AI models are deployed as static, opaque add-ons, while their data pipelines are treated as implementation details rather than controllable system assets. We address a central challenge: under dynamic intents and resource constraints, how can deployed RAN AI models and pipelines be treated as controllable objects, enabling observability, reconfigurability, and governance? We propose AI2N-RAN, an agentic intent-to-network architecture that couples a slow intent-to-policy loop with a fast evidence-to-action loop, unified by an assurance-and-learning plane. AI2N-RAN defines explicit interfaces to compile intents into auditable policies and monitoring specifications, and is designed to support evidence-driven, budget-aware reconfiguration with risk-aware governance across network, model, and data states. Using beam prediction as an illustrative use case, we instantiate selected AI2N-RAN interfaces and evaluate a governed model–data adaptation workflow using logged real-network measurements. Across selected model families, reconstruction-enabled candidates improve Top-1 accuracy by 21.03–42.30 percentage points and reduce selected-run training latency by 45.35%–48.39%. Additional measurements of training and inference costs expose configuration-dependent resource trade-offs and illustrate how the proposed architecture supports evidence-based candidate screening.
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