In this series of posts, I will outline the most common patterns for how artificial intelligence and machine learning are used in event driven architectures.
I’m at a Kafka / Flink conference this week.

This morning, I gave a talk about how AI and ML are used with Kafka topics. I had a lot to say, so I’ll write it up over the next few days:
- the building blocks used in AI/ML Kafka projects (this post)
- how AI / ML is used to augment event stream processing
- how agentic AI is used to respond autonomously to events
- how events can provide real-time context to agents
- how events can be used as a source of training data for models

In this first post, I’ll outline the building blocks available when bringing AI into the event-driven world, and discuss some of the choices that are available for each block.





