Coherent Generative Agentic AI Enabled 6G/7G Networking

Generative Artificial Intelligence (Generative Agentic AI) models, such as Large Language Models (LLMs), and agentic AI have recently gained widespread attention in both research and industry. They have rapidly emerged as a concept that elevates Generative Agentic AI from passive text generators to the next generation of agentic AI as autonomous, goal-driven agents capable of perception, planning, and dynamic action execution—Coherent Generative Agentic AI (CGA). In addition, CGA provides supervisory intelligence by maintaining persistent, evolving internal world models of the environment, networks, resources, agents, user intents, and policies. CGA reasons over time, runs long-horizon plans, checks consequences against its internal models, and coherently orchestrates lower-level controllers. It sits above network controllers, providing a global, semantic, model-driven brain. The paper starts with a review of CGA functional and non-functional characteristics in the context of key research and development results of agentic AI. It introduces a coherence-first architectural construct and system variable across all future 6G/7G networking planes (data-, transport-, management and operations,- orchestration,- autonomicity-, programmability-, and AI_as_a_Service-plane). It proposes a new deterministic CGA mathematical (CGAM) model that provides a new abstraction and theoretical foundation for analyzing the systemic consequences of different structural design and optimization options. It defines the relation between coherence and intelligence. The CGAM model represents the compute–communications continuum as a partially observable, intent-driven, contract-governed, multi-agent dynamical system with persistent memory and coherence constraints. We also included detailed CGAM validation and evaluation measures. This paper explores and details the CGAM’s role in 6G and 7G networking, as well as the challenges and opportunities for future work they present. We present the CGA-enabled 6G networking as well as 7G networking. Our investigation maps the current development landscape and points to future research and development in using Generative Agentic AI for networking.