AI-RAN consolidates AI services and Radio Access Network (RAN) functions onto a unified, GPU-accelerated infrastructure at the network edge. However, compute sharing between real-time RAN functions and highly heterogeneous AI services requires coordination of scheduling decisions at mismatched timescales, and placement adaptation may require service migration across nodes with non-negligible interruptions. This paper proposes a hierarchical agentic framework (HAF) for compute sharing in AI-RAN that combines a large language model (LLM)-based agent for slow-timescale placement of AI services and RAN functions with a closed-form, deadline-aware convex algorithm for fast-timescale GPU/CPU allocation. The LLM agent is further equipped with a predictive critic that filters out migrations when the induced service interruption outweighs the expected service-level objective (SLO) benefit. Experimental results show that HAF reaches 90.0% overall SLO fulfillment, a 20.5% improvement over the strongest baseline, and raises AI service request fulfillment from 51% to 85.3%. Further evaluations show that HAF retains its advantage under diverse load conditions, while the critic consistently improves SLO fulfillment across multiple open-source LLM agents.

翻译：AI-RAN将AI服务和无线接入网络（RAN）功能整合到网络边缘统一的GPU加速基础设施上。然而，实时RAN功能与高度异构的AI服务之间的计算共享需要在不同时间尺度上协调调度决策，且部署位置适配可能引发跨节点服务迁移并造成不可忽略的中断。本文提出一种用于AI-RAN计算共享的分层智能体框架（HAF），该框架结合基于大语言模型（LLM）的智能体负责慢时间尺度下AI服务与RAN功能部署，以及一个闭环式的截止时间感知凸优化算法用于快时间尺度GPU/CPU分配。该LLM智能体进一步配备预测性评判器，当服务迁移引发的服务中断超过预期服务等级目标（SLO）收益时，自动过滤此类迁移。实验结果表明，HAF总体SLO满足率达90.0%，较最强基线提升20.5%，并将AI服务请求满足率从51%提升至85.3%。进一步评估显示，HAF在多种负载条件下保持优势，而评判器在多个开源LLM智能体上均持续改善SLO满足率。