Intelligent 6G Networks: From Connected Things to Connected Cognition—An Agentic-Native Framework
Abstract
Sixth generation (6G) wireless networks aim to move beyond connected devices toward a “connected cognition” model in which the network understands intent, reasons about constraints, and executes actions autonomously. This article proposes an Agentic-Native 6G architecture embedding intelligence directly into network functions via three pillars: a distributed Knowledge Plane that lifts multimodal telemetry into semantic knowledge graphs within a Semantic Knowledge Base (SKB); autonomous agents equipped with perception modules, Recurrent State Space Model world models, Linear Temporal Logic guardrails, and Large Action Models (LAMs); and an Agent-to-Agent fabric for collaborative semantic inference. We clarify the hierarchy of Knowledge Plane, SKB, and knowledge graphs; distinguish LAMs from LLMs across training corpus, output modality, and action space; and quantify computational overhead. A proof-of-concept O-RAN implementation for industrial robot control outperforms KDN-style heuristics, model-free reinforcement learning, and recent world-model and intent-based baselines across KPI forecasting, latency control under jamming, and out-of-distribution adaptation.