Generative AI agents are set to transform business operations by autonomously managing tasks like customer service and logistics, but their effectiveness hinges on real-time data access. The concept of a "context gap" describes the issue where agents make decisions based on outdated information, leading to failures in scenarios such as rebooking flights for premium passengers. Real-time context engines address this by processing events and data as they occur, allowing agents to make informed decisions swiftly, as demonstrated in a hypothetical airline scenario where a quick response enhances customer satisfaction. This shift to real-time processing is facilitated by new tools like DeltaStream, which simplifies the integration of live data streams into AI systems, democratizing access to real-time data processing for developers without requiring deep expertise in complex systems. This evolution enables AI agents to operate effectively with up-to-date information, significantly improving their ability to make intelligent, context-aware decisions.