Today, E-commerce sellers face several key challenges, including difficulties in discovering and effectively utilizing available programs and tools, and struggling to understand and utilize rich data from various tools. We therefore aim to develop Insight Agents (IA), a conversational multi-agent Data Insight system, to provide E-commerce sellers with personalized data and business insights through automated information retrieval. Our hypothesis is that IA will serve as a force multiplier for sellers, thereby driving incremental seller adoption by reducing the effort required and increase speed at which sellers make good business decisions. In this paper, we introduce this novel LLM-backed end-to-end agentic system built on a plan-and-execute paradigm and designed for comprehensive coverage, high accuracy, and low latency. It features a hierarchical multi-agent structure, consisting of manager agent and two worker agents: data presentation and insight generation, for efficient information retrieval and problem-solving. We design a simple yet effective ML solution for manager agent that combines Out-of-Domain (OOD) detection using a lightweight encoder-decoder model and agent routing through a BERT-based classifier, optimizing both accuracy and latency. Within the two worker agents, a strategic planning is designed for API-based data model that breaks down queries into granular components to generate more accurate responses, and domain knowledge is dynamically injected to to enhance the insight generator. IA has been launched for Amazon sellers in US, which has achieved high accuracy of 90% based on human evaluation, with latency of P90 below 15s.

翻译：当前，电子商务卖家面临若干关键挑战，包括难以发现并有效利用现有程序与工具，以及在理解和运用各类工具产生的丰富数据方面存在困难。为此，我们致力于开发"洞察代理"——一种基于对话的多代理数据洞察系统，旨在通过自动化信息检索为电商卖家提供个性化数据与商业洞察。我们的假设是，洞察代理将成为卖家的效能倍增器，通过降低所需努力并提升卖家做出优质商业决策的速度，从而推动卖家采用率的增长。本文介绍这一基于大语言模型的新型端到端代理系统，其建立在规划-执行范式之上，并设计用于实现全面覆盖、高准确性与低延迟。该系统采用分层多代理架构，包含管理代理与两个工作代理（数据呈现与洞察生成），以实现高效信息检索与问题解决。我们为管理代理设计了一种简洁而有效的机器学习解决方案，结合使用轻量级编码器-解码器模型进行域外检测，以及通过基于BERT的分类器实现代理路由，从而在准确性与延迟之间达到优化。在两个工作代理内部，我们为基于API的数据模型设计了策略性规划机制，将查询分解为细粒度组件以生成更精准的响应，并通过动态注入领域知识来增强洞察生成能力。洞察代理已面向美国亚马逊卖家上线，基于人工评估达到90%的高准确率，P90延迟低于15秒。