AI is quickly becoming a must-have for businesses that want to stay ahead. To move faster, most teams are adding AI features to apps they already have. Microservices really help here—they break apps down into smaller, independent parts, which lets developers mix languages (like Java for the heavy lifting, and Python for AI and machine learning). This also makes it simpler to roll out and update new AI features as needs change.

In this session, we'll share what we've learned working with developers on real enterprise projects powered by AI. We’ll talk about practical topics like figuring out the right number of microservices, keeping up with fast-changing AI models, designing event-driven systems so agents can talk to each other, and managing workflows that blend traditional code with AI-driven components. We’ll also touch on smart ways to handle your data, so you get the right balance between keeping things separate and sharing what you need—while making sure everything stays consistent.

We'll go over design patterns that make it easier to build with AI—like how to organize services so you can trust AI-generated code, and tips for minimizing hallucinations. While we won’t have live demos during the session, we’ll share links to hands-on labs and code examples you can try afterwards. Our examples will use Spring, Java, and Oracle Database, but these ideas work across lots of tech stacks and programming languages.