Your Model Is Fine. Your Context Is Broken.

AI systems don’t fail because models are weak. They fail because context is wrong.

As agentic AI moves from experimentation to production, teams are discovering a new class of data problems. Context is scattered across operational databases, event streams, APIs, and vector stores. It’s stale by the time it reaches the model, inconsistent across tools, and expensive to recompute. Most architectures were never designed to continuously assemble and serve context at runtime.

This talk introduces context engineering as a practical, systems-level discipline focused on solving these data challenges. Rather than treating context as static input, context engineering treats it as a continuously computed product, derived from live business signals, enriched in real time, governed, and served with low latency to AI systems.

We’ll focus on the role of streaming and event-driven architectures as the foundation for this approach. You’ll see why batch pipelines and warehouse-centric designs struggle with agent workloads, and how stream processing enables data enrichment and reprocessing of context as data evolves.

In the second half, we’ll build this live. Using Kafka and Flink, we’ll construct a real-time context pipeline that ingests multiple data sources, enriches and materializes them into low-latency tables, and exposes them to AI agents through MCP.

This session is for engineers who want to move AI systems out of POCs and into production by fixing the data foundation first.