The automotive and mobility industry is undergoing significant transformation driven by advancements in vehicle connectivity, autonomous systems, and electrification. Vehicles are increasingly sophisticated machines that generate massive amounts of data, driving demand for connected and electric vehicles. Managing fleets of connected vehicles can be a challenge due to the increasing volume of data they produce and receive. To address this, fleet management software providers are investing in artificial intelligence (AI) and agentic AI, which enables proactive approach to route optimization and driver safety enhancement. Agentic AI agents process real-time traffic updates and weather conditions to dynamically adjust routes, ensuring timely deliveries while advising drivers on their car condition. The flexible document model of MongoDB is uniquely suited to handle both structured and unstructured data as vectors, making it an ideal foundation for agentic AI-driven architectures in the mobility sector. By leveraging MongoDB's scalability and flexibility, built-in vector search, time series collections, and real-time data processing capabilities, developers can create a single-view representation of an agent's state and behavior, simplifying development and enabling fast retrieval. The integration of agentic AI with MongoDB Atlas enables proactive approach to predictive maintenance and proactive decision-making in fleet management.