Speaker

Java is fundamentally changing. Enterprises deploying to Kubernetes now demand nanosecond startup times, minimal memory footprints, and fully optimized containers. This advanced session goes beyond basic JVM tuning to explore the cutting edge of Java modernization for cloud-native deployment. We’ll provide a deep dive and comparative analysis of optimization techniques, including Jib for minimal image creation, utilizing GraalVM Native Image for incredible cold-start acceleration, and leveraging the CRaC project (Coordinated Restore at Checkpoint) for state-of-the-art responsiveness. Join this hands-on Cloud Native Experience walkthrough to see live demonstrations of complex configuration patterns, detailed trade-off discussions, and actionable strategies for dramatically improving the cost-efficiency and velocity of your containerized Java microservices.

Thanks to open source, in the past year, we’ve seen a fundamental change: developers and enterprises are moving away from proprietary, closed-source models. To save costs, prioritize privacy, and allow for customization, they are building, testing, and deploying their own open models. However, this journey can feel overwhelming. Which foundation model should I use? How do I connect my model to existing data sources or build agentic capabilities to start seeing real value with AI, especially in an already existing Java application?

The key to navigating this emerging path is adopting the flexibility, transparency, and collaboration of open source that many of us are familiar with. We'll walk through the critical aspects of AI feature implementation using LangChain4J, also showing observability (OpenTelemetry), testing (Promptfoo), CI/CD (Tekton) and more. Join us as we get hands-on with language models and use open technologies to control our own AI journey!

Developing AI applications today goes beyond simple single-model interactions. We're seeing a shift towards agentic AI systems, where multiple specialized agents work together, each capable of independent reasoning. While there’s a myriad of ‘low-code’, ‘citizen developer’, and vendor-backed solutions, the real challenge for an AI architect lies in effectively building, orchestrating, and deploying these custom, collaborative systems to production. Despite all the promises from “AI” vendors, actual custom software development and a robust deployment strategy are essential for success.

Given the diverse and complex tasks these systems handle, a "one-size-fits-all" approach to coordination just doesn't work. We'll explore the spectrum of agentic patterns, from reliable but rigid workflows to highly flexible, autonomous agent orchestration using LLMs, and everything in between.

This talk will cover:

* The pros and cons of various agentic patterns.

* Practical demonstrations of how to combine these patterns using LangChain4j and its Quarkus extension.

* How to leverage the provided infrastructure for agent collaboration.

* The flexibility to implement and seamlessly integrate your own custom agentic patterns.