Beyond Prompting: Context Engineering and Memory Management for AI Systems at Scale

As AI agents evolve from stateless prompt-response tools into stateful, long-running systems, context - not just compute - becomes the true bottleneck. Yet most architectures today treat context retrieval as an afterthought, bolting vector stores onto LLMs and hoping for the best. The result: brittle pipelines, runaway costs, and hallucinations born from memory mismanagement.

In this talk, we’ll explore a new approach: stream-native context engineering, powered by Apache Kafka and Apache Flink. By treating context as data in motion - continuously enriched, windowed, compacted, and served with low latency - we can build memory layers that scale with our agents and evolve with their understanding. We’ll dive into how stream processing primitives (state backends, RocksDB tuning, checkpoint strategies) can be repurposed for AI memory orchestration, and how to design architectures that separate ephemeral context from durable knowledge.

You’ll walk away with a practical blueprint for building context-aware AI systems - from ingestion to retrieval - and see why the next frontier of agentic intelligence won’t be decided in the model weights, but in the context pipeline that feeds them.