Foundation models have revolutionized artificial intelligence, yet their application in recommender systems remains limited by reasoning opacity and knowledge constraints. This paper introduces AgenticRAG, a novel framework that combines tool-augmented foundation models with retrieval-augmented generation for zero-shot explainable recommendations. Our approach integrates external tool invocation, knowledge retrieval, and chain-of-thought reasoning to create autonomous recommendation agents capable of transparent decision-making without task-specific training. Experimental results on three real-world datasets demonstrate that AgenticRAG achieves consistent improvements over state-of-the-art baselines, with NDCG@10 improvements of 0.4\% on Amazon Electronics, 0.8\% on MovieLens-1M, and 1.6\% on Yelp datasets. The framework exhibits superior explainability while maintaining computational efficiency comparable to traditional methods.

翻译：基础模型已经彻底改变了人工智能领域，然而其在推荐系统中的应用仍受限于推理不透明性和知识约束。本文提出了AgenticRAG，一种将工具增强基础模型与检索增强生成相结合的新型框架，用于实现零样本可解释推荐。我们的方法整合了外部工具调用、知识检索和思维链推理，从而构建出能够进行透明决策且无需任务特定训练的自适应推荐智能体。在三个真实世界数据集上的实验结果表明，AgenticRAG相较于现有先进基线模型取得了持续的性能提升，在Amazon Electronics、MovieLens-1M和Yelp数据集上NDCG@10指标分别提升了0.4%、0.8%和1.6%。该框架展现出卓越的可解释性，同时保持了与传统方法相当的计算效率。