The path to higher network autonomy in 6G lies beyond the mere optimization of key performance indicators (KPIs). While KPIs have enabled automation gains under TM Forum Levels 1--3, they remain numerical abstractions that act only as proxies for the real essence of communication networks: seamless connectivity, fairness, adaptability, and resilience. True autonomy requires perceiving and reasoning over the network environment as it is. Such progress can be achieved through \emph{agentic AI}, where large language model (LLM)-powered agents perceive multimodal telemetry, reason with memory, negotiate across domains, and act via APIs to achieve multi-objective goals. However, deploying such agents introduces the challenge of cognitive biases inherited from human design, which can distort reasoning, negotiation, tool use, and actuation. Between neuroscience and AI, this paper provides a tutorial on a selection of well-known biases, including their taxonomy, definition, mathematical formulation, emergence in telecom systems and the commonly impacted agentic components. The tutorial also presents various mitigation strategies tailored to each type of bias. The article finally provides two practical use-cases, which tackle the emergence, impact and mitigation gain of some famous biases in 6G inter-slice and cross-domain management. In particular, anchor randomization, temporal decay and inflection bonus techniques are introduced to specifically address anchoring, temporal and confirmation biases. This avoids that agents stick to the initial high resource allocation proposal or decisions that are recent and/or confirming a prior hypothesis. By grounding decisions in a richer and fairer set of past experiences, the quality and bravery of the agentic agreements in the second use-case, for instance, are leading to $\times 5$ lower latency and around $40\%$ higher energy saving.

翻译：实现6G更高网络自主性的路径，远不止于关键性能指标（KPIs）的单纯优化。尽管KPIs在TM Forum第1至3级水平下推动了自动化水平的提升，但它们仍是数值化的抽象指标，仅作为通信网络真实本质——无缝连接性、公平性、适应性与韧性——的间接表征。真正的自主性要求网络能够如实地感知并推理其环境。这一进展可通过具身人工智能（agentic AI）实现，其中基于大语言模型（LLM）的智能体能够感知多模态遥测数据、借助记忆进行推理、跨领域协商，并通过API执行动作以实现多目标优化。然而，部署此类智能体带来了源自人类设计的认知偏见挑战，这些偏见可能扭曲推理、协商、工具使用与执行过程。本文结合神经科学与人工智能领域，选取一系列已知偏见，就其分类、定义、数学表述、在电信系统中的显现方式及其通常影响的具身智能体组件进行教程式阐述。本教程还针对各类偏见提出了相应的缓解策略。文章最后提供了两个实际用例，分别探讨了6G网络切片间管理与跨域管理中若干典型偏见的产生机制、影响及缓解效果。特别地，引入了锚点随机化、时间衰减与拐点奖励等技术，以针对性应对锚定偏见、时间偏见与确认偏见。这避免了智能体固守初始的高资源分配提案，或过度依赖近期及/或符合先前假设的决策。通过将决策建立在更丰富、更公平的历史经验基础上，例如在第二个用例中，具身智能体协商的质量与胆识使得时延降低了5倍，节能效果提升了约40%。