Generative AI has taken the world by storm over the last year, and it seems like every executive leader out there is telling us “regular” application developers to “add AI” to our applications. Does that mean we need to drop everything we’ve built and become data scientists instead now?

Fortunately, we can infuse AI models built by actual AI experts into our applications without too much pain. Thanks to the ease of use and superb developer experience of Quarkus (a Java stack) and the nice AI integration capabilities that the LangChain4j libraries offer, it becomes trivial to start working with AI and make your stakeholders happy :)

In this session, you’ll explore how with a few lines of code, you can add a variety of AI capabilities: prompting, chaining, and preserving state; agents and function-calling; enriching your AI model’s knowledge with your own documents using retrieval augmented generation (RAG); In addition, we'll add observability and fault tolerance to the AI integration.

What about "Agentic AI" and "MCP" clients and servers? No worries, we'll show you how to create those as well!

Come to this session to learn how to build Agentic AI-infused applications with Java, LangChain4j and Quarkus.

PS. Not a Java developer? Allow us to blow your mind and show you that Quarkus is not your grandma's Java.