Agentic Open RAN: A Deterministic and Auditable Framework for Intent-Driven Radio Control
For Open RAN operators, this provides a deterministic and auditable framework for intent-driven radio control, addressing the challenge of integrating LLMs into real-time network management.
A1gent introduces an agentic RAN control stack that decouples LLM-based reasoning from real-time actuation, enabling intent-driven radio control with deterministic execution and conflict governance. The framework achieves predictable adaptation without retraining, advancing Open RAN toward verifiable and self-governing agentic intelligence.
Large language models (LLMs) open new possibilities for agentic control in Open RAN, allowing operators to express intents in natural language while delegating low-level execution to autonomous agents. We present A1gent, an agentic RAN control stack that decouples reasoning from real-time actuation. A non-RT agentic rApp compiles operator goals into typed A1 policy instances, and three task-oriented near-RT agentic xApps enforce them through a deterministic loop with plane-scoped actuation - E2 for mobility and load steering, and O1 for energy orchestration. This agentic reasoning-execution split ensures auditable coordination between RAN intelligent controller (RIC) tiers, supported by encoded guardrails and a fixed-priority action merger for conflict governance. A training-free adaptive policy tuner then refines bounded parameters using KPI memory without retraining, sustaining predictable adaptation. By integrating intent-driven planning with deterministic near-RT execution, A1gent advances Open RAN toward verifiable, self-governing, and reproducible agentic intelligence.