AGENS: Agentic Link Construction for Environment and Intent Aware 6G Communication

Abstract

The emergence of diverse intelligent services is driving 6G networks toward an intelligent communication ecosystem with increasingly complex operating scenarios. However, existing AI-based communication methods mainly focus on isolated tasks or individual physical-layer mod- ules, which is insufficient for AI-native 6G systems that must jointly perceive dynamic environments, understand specific user intents, coordinate multiple coupled PHY-layer modules, and remain compatible with existing communication infrastructures. To address this gap, this paper, for the first time, explores auto- matic environment- and intent-aware full-link construction, which aims to generate executable PHY-layer configurations according to channel conditions and service requirements. We propose AGENS, an agentic communication decision-making framework for AI-native 6G link construction. Rather than replacing the existing PHY-layer protocol stack with a black-box end-to-end model, AGENS operates on top of the modular communication framework and jointly optimizes end-to-end PHY-layer configu- rations. Specifically, AGENS encodes CSI and natural-language user instructions, aligns communication states with user intents through cross-modal fusion, and leverages a pretrained LLM for intent-aware reasoning. An LSTM-based sequential action generator further captures dependencies among coupled sub- actions and produces structured full-link strategies. To enable stable training in the high-dimensional discrete action space, AGENS adopts a two-stage learning framework, where heuristic supervised initialization is followed by multi-objective reinforce- ment learning to jointly optimize reliability, throughput, and power consumption.