The path to higher network autonomy in 6G lies beyond the mere optimization of key performance indicators (KPIs), requiring systems that perceive and reason over the network environment as it is. This can be achieved through agentic AI, where large language model (LLM)-powered agents utilize multimodal telemetry, memory, and cross-domain negotiation to achieve multi-objective goals. However, deploying such agents introduces cognitive biases inherited from human design, which can severely distort reasoning and actuation. This paper provides a comprehensive tutorial on well-known cognitive biases, detailing their taxonomy, mathematical formulation, emergence in telecom systems, and tailored mitigation strategies. We validate these concepts through two distinct use-cases in 6G management. First, we tackle anchoring bias in inter-slice resource negotiation. To overcome the prohibitive execution delays of cloud-based LLMs, this use-case deploys a locally hosted 1B-parameter model on an RTX A4000 GPU, successfully achieving sub-second inference latencies compatible with near-real-time operations. By replacing fixed heuristic anchors with a Truncated Weibull randomized anchor strategy, the agents dismantle rigid biases, intelligently consume SLA slack, and dynamically double the system-wide energy savings (peaking at 25\%) without violating strict latency limits. Second, we mitigate temporal and confirmation biases in RAN-Edge cross-domain negotiation by designing an unbiased collective memory. By integrating semantic/temporal decay and an inflection bonus that actively highlights past negotiation failures, agents are prevented from over-relying on recent data or repeating past mistakes. Grounding decisions in this richer, debiased historical context yields highly robust agreements, achieving a $\times 5$ latency reduction and roughly 40\% higher energy savings compared to memoryless baselines.

翻译：通往6G更高网络自主性的道路超越了对关键性能指标（KPI）的简单优化，要求系统能够如实地感知和推理网络环境。这可通过智能体AI实现，其中基于大语言模型（LLM）的智能体利用多模态遥测、记忆和跨域协商来达成多目标。然而，部署此类智能体会引入继承自人类设计的认知偏差，这些偏差可能严重扭曲推理与执行。本文提供了一份关于常见认知偏差的全面教程，详细阐述其分类体系、数学形式化、在电信系统中的涌现机制以及针对性缓解策略。我们通过6G管理中的两个不同用例验证这些概念。首先，我们解决切片间资源协商中的锚定偏差。为避免基于云端的LLM带来的过高执行延迟，该用例在RTX A4000 GPU上部署了本地托管的10亿参数模型，成功实现了亚秒级推理延迟，满足近实时操作要求。通过将固定启发式锚定策略替换为截断威布尔随机锚定策略，智能体打破了刚性偏差，智能地利用服务等级协议（SLA）松弛，在不违反严格延迟限制的情况下，动态地将系统级节能效果翻倍（峰值达25%）。其次，我们通过设计无偏集体记忆来缓解无线接入网与边缘跨域协商中的时间偏差和确认偏差。通过整合语义/时间衰减机制以及主动突出历史协商失败的反转激励，可防止智能体过度依赖近期数据或重复过往错误。将决策建立在此类更丰富、去偏的历史背景之上，可获得高度稳健的协议，与无记忆基线相比，延迟降低5倍，节能效果提升约40%。