This work introduces Autogenic network management, a reference architecture that extends agentic capabilities with self-programming, self reflection, self reflection, self-orienting, and self-architecting capabilities, and concludes with a research roadmap outlining the technical advances needed to make autogenic network management realistic in future 6G networks.
Abstract
Standards bodies, including TM Forum, 3GPP, and ETSI, are converging on Agentic AI as the foundation for next-generation network management, where Large AI Model (LAM)-based agents autonomously interpret intent, coordinate resources, and adapt operational behaviors at runtime. However, achieving this vision at the scale and complexity of 6G networks requires management systems that can generate and evolve their own automation software during operation. We introduce Autogenic network management, a reference architecture that extends agentic capabilities with self-programming, self reflection, self-orienting, and self-architecting capabilities. The architecture supports practical staged deployment beginning with human-supervised LAM-based agents and progressing toward autonomous operation as confidence builds. We demonstrate the approach through high-priority operator scenarios drawn from TM Forum's autonomous network use cases, showing how autogenic management addresses real operational challenges. We conclude with a research roadmap outlining the technical advances needed to make autogenic network management realistic in future 6G networks.
Agentic Artificial Intelligence (AI), enabled by Large Language Models, marks a shift from rule-based automation toward autonomous, goal-driven control of Next-Generation Networks (NGNs). Existing surveys treat the two domains in isolation, leaving protocol integration, evaluation, and standardization alignment underexplored. To address this gap, a two-part tutorial-and-survey is presented. Part I formalises the control, management, and AI-native planes of 5G and 6G. It then covers the foundations of agentic systems: reasoning, planning, tool use, multi-agent coordination, and evaluation. Part II maps agentic capabilities onto 5G/6G control surfaces, standardization, and major 6G initiatives. Finally, it identifies open challenges shaping autonomous telecommunications.
Mazene Ameur, Abdelkader Mekrache, Bouziane Brik et al.· 0 citations
The evolution towards 6G edge-cloud ecosystems demands autonomous, intent-based network management to handle unprecedented infrastructure complexity. While Large Language Models offer promising capabilities for translating high-level user intents into network configurations, current monolithic approaches suffer from cognitive overload, hallucinations, and a profound inability to safely execute low-level data plane mutations. To bridge this gap, we introduce Edgent, a novel framework that integrates hierarchical Agentic AI with Extended Berkeley Packet Filter technologies via the Model Context Protocol. Edgent utilizes a state-driven Supervisor, enhanced by Retrieval-Augmented Generation, to decompose abstract human intents into deterministic execution graphs and dynamically delegate tasks to domain-specific worker agents. We empirically validate the framework by autonomously deploying a distributed, in-kernel DDoS mitigation pipeline across scaled containerized topologies containing up to 85 nodes. Extensive evaluations demonstrate high orchestration reliability; notably, even heavily quantized Small Language Models (e.g., 4B parameters) achieve near-perfect zero-shot execution and 100% overall task completion through autonomous error recovery. Finally, latency and resource profiling confirm that the multi-agent framework can be efficiently driven by fully localized models compatible with orchestration tasks directly within resource-constrained edge environments, therefore this work positions Edgent as a pragmatic step toward the realization of zero-touch nextgeneration networks.
Raffaele Di Tommaso, G. Davoli, Pietro Spadaccino et al.· IEEE Conference on Network S...· 0 citations
Experimental results show that the proposed self-calibrating agentic framework successfully profiles the zero-knowledge workloads, achieving a higher accuracy than baseline LLM agents and establishing a robust foundation for deploying autonomous AI in decentralized infrastructures.
Fin Gentzen, Marla Grunewald, Iulisloi Zacarias et al.· 0 citations
Agentic AI is emerging as a promising paradigm for network management, enabling high-level intent processing, automated decision making, and closed-loop control. However, the current landscape is fragmented: proposed solutions are often evaluated in ad hoc settings, with limited reproducibility and no common basis for systematic comparison. This lack of benchmarking methodology makes it difficult to assess the actual benefits, limitations, and operational trade-offs of different agentic approaches. This paper presents a playground for benchmarking agentic AI in network management. Rather than proposing a single best-performing agent, the goal is to provide a controlled and extensible environment in which heterogeneous agentic solutions can be deployed, observed, and compared under common network management tasks. The playground combines a programmable multi-node network topology, a transaction-oriented control workflow, structured agent-to-network interfaces, explicit network state representation, and built-in validation and rollback mechanisms. Its design enforces a clear separation between high-level agent reasoning and deterministic execution, thus enabling safer and more auditable experimentation. The proposed framework is instantiated over a network management scenario based on Segment Routing over IPv6 (SRv6), where agentic solutions interact with the infrastructure through declarative messages instead of arbitrary low-level commands. This design supports benchmarking along multiple dimensions, including task success, convergence behavior, robustness to failures, recovery capability, safety of issued actions, and auditability of the control process. By providing a reproducible and observable experimentation environment, the proposed playground lays the foundation for a systematic evaluation methodology for agentic AI in network management.
Stefano Salsano, A. Mayer, Lorenzo Bracciale et al.· La Main· 0 citations
The 6G era introduces unprecedented complexity in managing heterogeneous, large-scale, and dynamic network infrastructures. These challenges are addressed by the concept of Intent-Based Networking (IBN), which has emerged as a promising paradigm for autonomous network management, enabling users to express high-level objectives that are automatically translated and enforced by the network. However, current IBN solutions remain constrained by rigid structured specifications and limited assurance mechanisms. This paper presents an overview of PhD research leveraging Generative AI (GenAI), specifically Large Language Models (LLMs), to address three fundamental IBN challenges: (i) intent translation, (ii) intent assurance, and (iii) GenAI operations in IBN systems. We propose a set of novel frameworks validated on real 5G/6G testbeds. Most contributions are supported by demos, datasets, and open-source implementations, which are referenced in the design section of each contribution. This PhD positions GenAI as a key enabler for advancing autonomous and user-centric 6G.
Large Language Model (LLM)–based agents are rapidly evolving from passive assistants into autonomous, tool-using, and collaborative systems capable of executing complex, long-horizon tasks across web, software, and physical environments. However, the current literature remains fragmented, with inconsistent terminology, ad hoc architectures, and limited evaluation standards, making it difficult to compare systems or deploy them reliably in real-world settings. This paper presents a unified, taxonomy-driven, and deployment-oriented survey of agentic AI systems, synthesizing recent advances through a modular reference architecture and a four-dimensional taxonomy that characterizes agents along the axes of autonomy, tool use, collaboration, and safety–governance. We systematically analyze representative single-agent, tool-augmented, and multi-agent frameworks within this taxonomy, highlighting design trade-offs, capability scaling patterns, and recurring failure modes. Beyond architectural analysis, we review emerging evaluation methodologies that move beyond static benchmarks to assess agent behavior, robustness, grounding, and operational cost in interactive environments. Importantly, the survey emphasizes practical considerations for enterprise and safety-critical deployment, including access control, human-in-the-loop oversight, and policy enforcement. By unifying conceptual foundations with empirical trends and deployment constraints, this work provides a structured roadmap for researchers and practitioners to design, evaluate, and govern next-generation LLM-based agentic systems.
Sparsh Bajoria, Shreyanshu Ranjan, Adhitya M et al.· Cognitive Computation· 0 citations