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，远不止于对关键性能指标（KPIs）的单纯优化。虽然KPIs已在TM Forum 1-3级水平上实现了自动化增益，但它们仍是数值抽象，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——的代理指标。真正的自主性要求感知并基于网络环境的本来面目进行推理。此类进展可通过**智能体人工智能**实现，其中由大语言模型（LLM）驱动的智能体能够感知多模态遥测数据、借助记忆进行推理、跨域协商，并通过API执行动作以实现多目标。然而，部署此类智能体带来了从人类设计中继承而来的认知偏差挑战，这些偏差可能扭曲推理、协商、工具使用和执行过程。本文立足于神经科学与人工智能之间，针对一系列众所周知的偏差提供了一个教程，涵盖其分类、定义、数学表述、在电信系统中的涌现方式以及通常受影响的智能体组件。本教程还针对每种偏差类型提出了多种定制化的缓解策略。文章最后提供了两个实际用例，探讨了6G网络切片间与跨域管理中一些著名偏差的涌现、影响及缓解收益。特别地，引入了锚点随机化、时间衰减和拐点奖励技术，以专门应对锚定偏差、时间偏差和确认偏差。这避免了智能体固守初始的高资源分配提议，或固守近期和/或证实先前假设的决策。通过将决策建立在更丰富、更公平的历史经验集合之上，例如在第二个用例中，智能体协议的质量和胆识使得延迟降低了5倍，节能效果提升了约40%。