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.
@article{arxiv.2510.02668,
title = {AgenticRAG: Tool-Augmented Foundation Models for Zero-Shot Explainable Recommender Systems},
author = {Bo Ma and Hang Li and ZeHua Hu and XiaoFan Gui and LuYao Liu and Simon Liu},
journal= {arXiv preprint arXiv:2510.02668},
year = {2025}
}