Future 6G networks will interconnect not only devices, but autonomous machines that continuously sense, reason, and act. In such environments, communication can no longer be understood solely as delivering bits or even preserving semantic meaning. Even when two agents interpret the same information correctly, they may still behave inconsistently if their internal reasoning processes evolve differently. We refer to this emerging challenge as belief divergence. This article introduces reasoning native agentic communication, a new paradigm in which communication is explicitly designed to address belief divergence rather than merely transmitting representations. Instead of triggering transmissions based only on channel conditions or data relevance, the proposed framework activates communication according to predicted misalignment in agents internal belief states. We present a reasoning native architecture that augments the conventional communication stack with a coordination plane grounded in a shared knowledge structure and bounded belief modeling. Through enabling mechanisms and representative multi agent scenarios, we illustrate how such an approach can prevent coordination drift and maintain coherent behavior across heterogeneous systems. By reframing communication as a regulator of distributed reasoning, reasoning native agentic communication enables 6G networks to act as an active harmonizer of autonomous intelligence.

翻译：未来的6G网络不仅将连接设备，还将连接能够持续感知、推理与行动的自主机器。在此类环境中，通信不再能仅被理解为比特传输甚至语义保持。即使两个智能体对同一信息的解读正确，若其内部推理过程演化不同，仍可能导致行为不一致。我们将这一新兴挑战称为信念分歧。本文提出推理原生智能体通信这一新范式，其通信设计明确以解决信念歧异为目标，而非仅传递表征。该框架不再仅基于信道条件或数据相关性触发传输，而是根据对智能体内部信念状态错位的预测来激活通信。我们提出一种推理原生架构，该架构在传统通信协议栈基础上，增加了一个基于共享知识结构与有界信念建模的协调平面。通过核心使能机制与典型多智能体场景分析，我们阐明该方法如何防止协调漂移，并在异构系统中维持行为一致性。通过将通信重新定义为分布式推理的调节机制，推理原生智能体通信使6G网络能够成为自主智能的主动协调器。