Multi-Agentic AI for Intent-Driven and Conflict-Aware rApp Policy Orchestration in Open RAN

Abstract

Open radio access network (O-RAN) enables a multi-vendor ecosystem that accelerates xApp development for near-real-time (RT) network management. However, this openness shifts a combinatorial coordination burden to the Non-RT layer, where rApp policies compose xApps from a growing catalog to realize operator intents. Coexisting intents further compound this challenge, as conflicts may arise among xApp actions, rApp policies, and their cross-layer interactions. To maximize intent admission while maintaining key performance indicator (KPI) compliance across incoming and incumbent intents, we propose a large language model-based Multi-Agentic AI framework. A Reasoning Agent screens candidate xApps against KPI contracts and produces a joint intent admission and xApp assignment decision. On post-deployment KPI violations, a Diagnosis Agent identifies the root causes of failure, labels the conflict mechanism, and guides the Reasoning Agent in subsequent attempts. Experimental results show that the framework based on GPT-OSS 120B achieves 28.6% more KPI-compliant cases and admits over 15* as many incoming intents as state-of-the-art baselines. Under the Qwen 72B variant, Diagnosis increases compliant cases by 50.0%, quadruples admitted intents, and reduces deployment attempts by 32.6%. A live deployment conforming to O-RAN specifications further validates the feasibility of the proposed solution for conflict-aware rApp policy orchestration in real networks.