摘要：维持价格一致性并执行“每日低价”（Every Day Low Price）战略，对全球零售商至关重要。然而，面对包含数百万在售商品的目录，人工管理商品之间的价格关系并不可行。商品变体之间的定价不一致会扭曲消费者对价值的认知，并造成销售蚕食。为解决这一问题，我们提出了一套可扩展、具备上下文感知能力的多 Agent 框架，用于自动构建“产品线与价格阶梯”（Lines and Ladders）定价分类体系。该框架利用专门化的 LLM Agent，通过识别关键属性、提取多模态价值信息以及应用层级分组逻辑，构建连贯的定价结构。我们在真实企业数据上对该框架进行了评估，并已将其部署至生产环境。3 Agent 系统在线（Lines）任务上取得了 0.83 的 F1 分数；通过缓解认知负荷，其表现优于单 Agent 基线。该系统在食品与消耗品类别中实现了超过 90% 的精确率和超过 75% 的召回率，并在非结构化综合商品目录中实现了 80.2% 的分配准确率。

Abstract:Maintaining price consistency and executing an Every Day Low Price strategy is critical for global retailers. However, with catalogs spanning millions of active items, manual governance of price relationships is infeasible. Inconsistent pricing across item variants distorts customer value perception and cannibalizes sales. To address this, we present a scalable, context-aware Multi-Agent Framework designed to automate the construction of "Lines and Ladders" pricing taxonomies. Our framework employs specialized LLM agents to construct these coherent pricing structures by identifying key attributes, extracting multi-modal values, and applying hierarchical grouping logic. Evaluated on real-world enterprise data and deployed in production, our 3-Agent system achieves an F1-score of 0.83 for Lines, outperforming single-agent baselines by mitigating cognitive overload. The system achieves >90% precision and >75% recall in Food & Consumables, and 80.2% assignment accuracy in the unstructured General Merchandise catalog.