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Highlights from JDConf 2026: Java Pattern Matching for AI Orchestration

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Building maintainable orchestration layers over uncertain AI outputs, ensuring clear control flow, traceability, and predictable system behavior.

I was genuinely excited to receive the acceptance email for my proposal to talk about the use of pattern matching in orchestrating AI agents. This talk shares many of the ideas that have been shaping much of my recent work. My talk was structured in two parts. The first part provides an overview of Java’s modern pattern-matching capabilities. The second part provides a practical exploration of how pattern matching can become especially powerful when orchestrating AI agents in real-world systems.

The Code Samples

You can find the code samples used in the slides available for download at https://drive.google.com/drive/folders/1cS0OK7clmELV77FWfp2BOcet-gCyLfne?usp=drive_link. The code samples that explain Pattern Matching in Java are available in the javapatternmatching.zip project. You can easily open this project using the intelliJ IDE.  The code samples demonstrating the usability of pattern matching for orchestrating AI agents are available in the other two projects. You can easily open them using the Kiro IDE. 

talk description
The Presentation of My Talk

Whether I’m delivering a talk at a meetup or a conference, I usually share the slides on Slideshare.net. In addition, you can find links to download these slides at https://lifemichael.com/en/talks, where I provide detailed information about where I delivered my talks. 

Pattern matching is well supported across many programming languages, including C#, Kotlin, Scala, and Python. Whether you are using Java to orchestrate AI agents or working with another language, you can most likely improve your code by leveraging pattern matching.

 
 
 

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