My tokens are limited. You must use the right context: An agent builders guide to Apache Flink

AI models can only process a limited amount of information (tokens) at once. This limits their ability to make smart, context-aware decisions for business operations. This talk shows how to use Apache Flink to give AI precise, custom context. I believe the key to better AI isn't just bigger models, but smarter, more efficient use of data. As a wise person might say, "An AI only knows what it's shown." Let's learn how to turn large amounts of data into useful context by: Managing Tokens Wisely: Carefully choosing data to get the most from every token. Better Vector Search: Looking at different tools, from special vector databases to using Flink ML for finding similar data directly in your data streams. Building Smart Agent Workflows with Flink: Using Flink's DataStream API, Complex Event Processing (CEP), and broadcast features to create advanced agent systems. These systems can adjust their context, remember information, and use preferences. Learn how Apache Flink helps build next-generation AI agents that work with sharp focus, using only the data that matters. We'll also briefly cover the technical details, showing why Flink's ability to handle data over time is vital for this new field.