Agentic AI for Ultra-Modern Networks: Multi-Agent Framework for RAN Autonomy and Assurance

Centralized AI systems in networks create safety risks: The paper demonstrates that traditional RIC-based (centralized) control in 5G/6G networks is vulnerable to "policy conflicts, data drift, and unsafe actions under unforeseen conditions," highlighting why distributed approaches may be safer.

Local optimization can harm the broader network: In their traffic steering experiment, a naive AI predictor improved local performance metrics (RRC connected users, IP throughput) but "destabilizes neighbors," showing that autonomous AI systems optimizing for single objectives can inadvertently damage overall network health.

Multi-agent systems can prevent unsafe AI decisions: The proposed distributed multi-agent framework successfully "blocks unsafe policies" and "preserves global network health," suggesting that having specialized agents verify and validate each other's decisions provides a practical safeguard against harmful AI actions.

Explainability and resilience improve with distributed AI: By replacing tightly-coupled centralized workflows with distributed agents handling "data collection, model training, prediction, policy generation, verification, deployment, and assurance," the system achieves better "explainability and system-wide safety"—making it easier to understand and trust AI decisions in critical infrastructure.

Verification mechanisms are essential for autonomous networks: The framework's ability to evaluate policies before deployment across four key performance indicators suggests that built-in verification steps are crucial for responsible AI deployment in complex, interconnected systems.