AI 中文总结
SAFAARI是用于加速广告商响应智能的多智能体框架，通过专门智能体解决NL-to-SQL系统模式链接瓶颈，引入SEAL评估性能。实验显示其SEAL分数达81.66%，开发时间减少8倍，经专家评估有效，提升了自助服务能力，助力复杂数据生态企业。
AI 中文摘要
客户支持系统随着智能聊天机器人迅速发展，但在访问无预定义API端点的企业数据时面临重大限制。本文提出了SAFAARI，一个多智能体框架，通过专门的内容、元数据和编排智能体解决自然语言到SQL（NL-to-SQL）系统中模式链接的关键瓶颈。还引入了SEAL来整体评估系统性能。通过对五种特征集配置的系统实验，SAFAARI的SEAL分数达到81.66%。通过与领域专家的人在回路评估验证了框架的有效性，自动化模式链接和查询生成过程使开发时间减少8倍，提高了自助服务能力，有利于复杂数据生态系统的客户支持企业。
英文摘要
The evolution of customer support systems is rapidly advancing with agentic chatbots, yet these systems face significant limitations when accessing enterprise data without predefined API endpoints. This paper presents SAFAARI (Schema-Aware Framework for Accelerated Advertiser Response Intelligence), a multi-agent framework that addresses the critical bottleneck of schema linking in Natural Language to SQL (NL-to-SQL) systems through specialized content, metadata, and orchestration agents. We also introduce SEAL (Schema Evaluation and Accuracy in Language-to-SQL), a novel composite metric that holistically evaluates system performance while penalizing inconsistent results. Through systematic experimentation with five feature set configurations, SAFAARI achieves an 81.66% SEAL score (6.65% improvement over baseline), with notable gains in datapoint accuracy (5.51%) and schema-linking precision (4.69%). The framework's effectiveness is validated through human-in-the-loop evaluation with domain experts, which proves its adaptability across diverse support domains. By automating the labor-intensive process of schema linking and query generation, our framework demonstrates 8x reduction in development time while maintaining high accuracy. The solution streamlines API development and enhances self-service capabilities, particularly benefiting customer support enterprises with complex data ecosystems.
