Traditional e-commerce search often suffers from high abandonment rates due to an intent gap where users provide broad or vague queries. We propose a novel framework using Agentic AI to bridge this gap through dynamic query reformulation. By leveraging the ReAct agentic framework, our system performs contextual reasoning by analyzing historical purchase data to transform generic queries into hyper-personalized search queries. Unlike personalization methods that require architectural overhauls, our solution operates on top of existing search systems, ensuring seamless integration with any search system without costly development or downtime. To ensure low-latency performance, we utilize offline pre-computation of top queries and real-time cosine similarity matching. Results from our experiments demonstrate that this agentic approach can significantly improve search ranking leading to improved customer engagement.