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arXiv · 2605.20608

From Automated to Autonomous: Hierarchical Agent-native Network Architecture (HANA)

Binghan Wu·Shoufeng Wang·Yunxin Liu·Ya-Qin Zhang·Joseph Sifakis·Ye Ouyang
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Abstract

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.

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Binghan Wu, Shoufeng Wang, Yunxin Liu, Ya-Qin Zhang, Joseph Sifakis, Ye Ouyang. 2026-05-20. From Automated to Autonomous: Hierarchical Agent-native Network Architecture (HANA). https://doi.org/10.1109/lnet.2026.3693226

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