From Automated to Autonomous: Hierarchical Agent-native Network Architecture (HANA)
The paper presents a new architecture called Hierarchical Agent-native Network Architecture (HANA) aimed at achieving higher levels of autonomy in networks. It emphasizes the transition from static automation to agent-native intelligence, addressing limitations in current operations. The proposed framework has been validated in a 5G Core environment, demonstrating significant improvements in operational resilience and efficiency.
- ▪The architecture enables high-level autonomy through a hierarchical multi-agent reference model.
- ▪A Dual-Driven Orchestrator coordinates specialized Executive Agents and utilizes a shared Public Memory.
- ▪The integration of agent self-awareness allows for better strategic governance and fault recovery.
- ▪Case studies show an 86% reduction in Mean Time to Repair (MTTR) under congestion.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20608 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
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| Summary source text | contentText |
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| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Artificial Intelligence arXiv:2605.20608 (cs) [Submitted on 20 May 2026] Title:From Automated to Autonomous: Hierarchical Agent-native Network Architecture (HANA) Authors:Binghan Wu, Shoufeng Wang, Yunxin Liu, Ya-Qin Zhang, Joseph Sifakis, Ye Ouyang View a PDF of the paper titled From Automated to Autonomous: Hierarchical Agent-native Network Architecture (HANA), by Binghan Wu and 5 other authors View PDF HTML (experimental) 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.
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