mlsanityData to 7 Oct 2026

Role-Based Agentic AI for Intent-Driven Network and Service Orchestration

arXiv 2606.20580 ↗First posted 6 May 2026cs.NI cs.AIcited by 0

On the map: LLM agents and tool use → Agentic AI for wireless networks

Abstract (arXiv)

Telecommunication networks are increasingly complex due to heterogeneous technologies, diverse service requirements, and growing demands for resource efficiency and business agility. Intent-Based Networking (IBN) and, more recently, agentic AI have emerged as promising paradigms to address this complexity through autonomous network management. However, existing approaches primarily focus on operational orchestration within Operations Support Systems (OSS) and lack an integrated framework that spans Business Support Systems (BSS) and OSS, limiting the realisation of true intent-to-business-to-network coordination. This paper presents a role-based multi-agent architecture (MAS) for end-to-end intent orchestration that mirrors Communication Service Provider (CSP) organisational structures. The proposed framework applies principles of functional decomposition, explicit task ownership, privacy-preserving domain separation, and domain-specific expertise within a hierarchical four-layer agent system spanning customer engagement, strategic planning, service delivery, and infrastructure provisioning. Leadership agents coordinate planning activities, whilst specialised service and resource agents are dynamically instantiated according to intent requirements. A proof-of-concept implementation demonstrates the feasibility of bridging the BSS-OSS divide through structured agent coordination, illustrating how agentic MAS can support accountable and scalable intent-driven service orchestration.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Agentic AI for wireless networks, one of 16 directions in LLM agents and tool use. Builds on 2 of its 9 references that are in the map. First posted on arXiv 6 May 2026.

Builds on

2 of 9 references.

References · 2

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  2. 2020Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorialcited by 59

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