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.

翻译：大语言模型为Open RAN中的智能体控制提供了新可能性，使运营商能够以自然语言表达意图，同时将底层执行委托给自治智能体。我们提出A1gent——一种将推理与实时执行解耦的智能体无线接入网控制栈。非实时智能体rApp将运营商目标编译为类型化A1策略实例，三种面向任务的近实时智能体xApp通过确定性循环及平面限定执行机制（E2平面负责移动性与负载均衡，O1平面负责能耗编排）来强制执行这些策略。这种智能体推理-执行分离机制确保了无线接入网智能控制器层级间的可审计协调，并通过编码护栏与固定优先级动作合并器实现冲突治理。随后，一种免训练的自适应策略调优器利用关键性能指标记忆在无需重训练的情况下优化带限参数，维持可预测的自适应能力。通过将意图驱动的规划与确定性近实时执行相结合，A1gent推动Open RAN迈向可验证、自治理且可复现的智能体智能。