Retails have undergone rapid transformation in recent years, driven by technological innovation and shifting consumer expectations. Among these technologies, agentic artificial intelligence (AI), defined by its capacity for autonomous decision making and purposeful action, is increasingly recognized as a key driver of innovation in retail. This systematic review synthesizes the current research on the applications of agentic AI in retail, with a particular focus on customer experience, operational efficiency, and strategic decision-making. A comprehensive search across four databases identified 2149 studies that were screened using the PRISMA guidelines, resulting in 19 papers meeting the inclusion criteria. The findings revealed that agentic AI enhances personalization through conversational agents and recommender systems, optimizes operations via inventory monitoring and demand forecasting, and informs strategic agility through adaptive pricing and market intelligence. However, significant challenges remain, such as consumer trust, algorithmic transparency, data governance, and workforce displacement. This review identifies research gaps in longitudinal studies on trust, cross-cultural adoption patterns, and hybrid human–AI retail models. Overall, agentic AI holds transformative potential for retail, but its value depends on balancing efficiency with ethics, personalization with privacy, and autonomy with accountability.