Applied AI for Enterprise Java: A Field Guide for the Next Generation of Developers

How real-world teams are bringing Generative AI, LLMs, and Java together to build production-ready intelligent systems.

It finally happened. After many early mornings, late nights, and countless cups of coffee, Applied AI for Enterprise Java is now available. You can grab the eBook on O’Reilly or Amazon.

Writing a book is a strange thing. It happens in the gaps of life. Between family dinners and customer calls. Between flights, releases, and all the daily noise that fills a developer advocate’s calendar. Some chapters were written in hotel rooms after long events. Others in the quiet hours before everyone else woke up. It never feels like the right moment to write, and yet somehow it happens.

This one is special. We wanted to make something that speaks to real enterprise developers. The kind of people who build things that actually run in production. Applied AI is not another theoretical introduction to machine learning. It is a practical field guide for Java developers and architects who want to bring AI into their systems without losing their sanity.

The book reflects what we see every day with customers. Teams exploring AI, experimenting with models, and then realizing the real challenge starts when the prototype works. How do you scale it? How do you keep it compliant? How do you make it observable and affordable? Those are the problems that matter in production, and they’re the ones we focus on.

Together with Alex Soto Bueno and Natale Vinto, we tried to extract what already works in the field. Patterns that keep the Java strengths of stability and modularity while opening the door to AI’s flexibility. We show how to integrate open source and commercial models, use Java’s Inference APIs, and take advantage of frameworks like Quarkus to run everything efficiently on modern infrastructure.

If you’ve been reading my articles here, you’ve already seen parts of that thinking in action. Posts like LangChain4j and Quarkus with local models or self-organizing AI memory systems are natural extensions of the book. They explore the same question: how can Java developers use their existing skills to build intelligent, production-ready systems?

Quarkus plays a big role in that story. It has quietly matured into one of the most ready frameworks for AI-infused workloads. Its developer experience makes it easy to experiment, while its native builds and build-time optimizations make those experiments deployable and fast. The combination of Quarkus and LangChain4j opens up a completely new playground for enterprise-grade AI applications.

All code examples and projects from the book are open source and live in the GitHub repository. We built it as a companion that will evolve alongside the book. The ecosystem moves quickly, and keeping the code current matters as much as the printed words.

Recently, I also wrote a three-part series for O’Reilly Radar called The Java Developer’s Dilemma.
Part one looks at how Java developers found themselves at a crossroads, balancing tradition with innovation.
Part two explores how AI is transforming what it means to be a Java developer, and how the skills that once defined us are evolving into new forms of system thinking.
And part three examines where we go from here: how platforms like Quarkus and frameworks like LangChain4j are reshaping the future of enterprise Java, making it not just relevant but essential in an AI-driven world.

Those essays and this book share the same heartbeat. They both come from the same question: how can we stay grounded in our craft while adapting to this new era of intelligent software? I believe Java has everything it needs to thrive here. Strong standards, a resilient community, and frameworks that evolve faster than anyone expects.

This book exists because of all the conversations we’ve had with developers and architects who are already on this journey. The people who run critical systems and now want to integrate intelligence without losing reliability. It’s a reflection of those real-world efforts.

And now, after all that work, it is out in the world.

If you decide to read it, I’d be incredibly grateful if you left a short review on Amazon or O’Reilly. It helps others discover it, and it means a lot to us as authors.

Also, if you’re still looking for the perfect Christmas present for that Java developer in your life, this book checks every box. It’s practical, it smells faintly of coffee and build logs, and it will look great next to your laptop.

Thanks for reading, thanks for being part of this journey, and thanks for keeping the Java world curious about what’s next in AI.

Markus