Explainable Agentic AI: Transforming E-Commerce Search With Transparency

Abstract

This new breed of agentic AI systems, the ability to act on their own, towards their goal, is transforming the digital commerce environment through fabulous intelligent search and recommendation applications. When integrated through conversational agents on e-commerce websites, these systems string together customer experiences, optimize the product discovery process, and personalize the compendium on a real-time basis. Yet, as these agents become more autonomous, explainability, transparency, and fairness become essential to trust by the users and regulatory compliance. The following article is about the incorporation of the practice of Explainable AI (XAI) in the agentic systems of retail search, specifically in conversational commerce. It presents the shortcomings of transparency in existing implementations and postulates a conceptual model of several mutually tied elements: the Goal Engine as an intention disambiguation system, the Perception Layer as a query interpreter, the Decision Core as a recommendation logic system, the Memory Store as the personalization auditing unit, and the Feedback Loop designed to evaluate performance. The article explains the methods of architectural implementation and the main capabilities that could be used in order to provide a trustworthy AI-based shopping experience. It ends with an inquiry on the business case of explainability, showing how explainable systems provide higher customer loyalty, lessening regulatory risk exposure, better debugging operations, and more effective personalization in e-commerce settings.