From Automated to Autonomous: Hierarchical Agent-native Network Architecture (HANA)
For network operators, it addresses the need for cognitive autonomy in network management, moving beyond static automation to handle off-nominal conditions.
The paper proposes a hierarchical multi-agent architecture for autonomous networks, demonstrating an 86% reduction in Mean Time to Repair (MTTR) while sustaining throughput under congestion in a 5G Core environment.
Realizing Level 4/5 Autonomous Networks (AN) demands a shift from static automation to agent-native intelligence. Current operations, reliant on rigid scripts, lack the cognitive agency to handle off-nominal conditions. To address this, this letter proposes a hierarchical multi-agent reference architecture enabling high-level autonomy. The framework features a Dual-Driven Orchestrator that coordinates specialized Executive Agents, supported by a shared Public Memory for unified domain knowledge. A key innovation is the integration of agent self-awareness, which empowers the system to harmonize deliberative strategic governance with reflexive fault recovery. We instantiate and validate this architecture within a 5G Core environment. Case studies demonstrate that the system sustains critical throughput under congestion and reduces Mean Time to Repair (MTTR) by 86%, confirming its efficacy in unifying strategic planning with operational resilience.