A novel framework, A-MAR, has been introduced to enhance fine-grained artwork understanding by leveraging agent-based multimodal art retrieval. This approach enables multi-step reasoning over visual content, as well as cultural, historical, and stylistic context, to provide more explicit and interpretable results. Unlike recent multimodal large language models, A-MAR promotes explicit evidence grounding, addressing the limitations of implicit reasoning and internalized knowledge. The framework's ability to facilitate more transparent and explainable artwork analysis has significant implications for various fields, including art history, conservation, and authentication1. As AI continues to advance in this domain, it is likely to have far-reaching consequences, extending beyond technology into areas such as policy, security, and workforce dynamics. The development of A-MAR underscores the importance of prioritizing transparency and interpretability in AI-driven art analysis, making it a crucial consideration for practitioners and researchers in the field.