Conference Sessions
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Java for an AI World
AI is reshaping how we build software—and Java is evolving across the platform to meet that opportunity. This keynote explores how Java’s language, libraries, and runtime provide …
Workshops / Work-a-thon
Agentic Java Development with Eclipse Theia AI: One mvn test at a Time
Speakers:
Developer tools are undergoing a major transformation with the integration of AI coding agents. Eclipse Theia AI provides an open, vendor-neutral framework for building AI-native …
Black Friday Meltdown: A Java Production War Game
A gamified 90-minute hands-on work-a-thon where participants step into the shoes of a production support engineering team during a high-stakes Black Friday outage! Rather than …
Building MCP Applications with Spring AI
Speakers:
AI applications are evolving beyond simple text chats to interact with real tools, APIs, and external systems. This hands-on workshop teaches Java developers how to build …
Deep Talks
Don’t Trust the Prompt: Secure AI with Java from Code to Agent
What happens when an AI assistant can do more than answer questions—when it can access enterprise data, invoke tools, remember conversations, and take actions on behalf of a user? …
Scale More, Spend Less: Modern Java to the Rescue in Production
Speakers:
Can your Java application handle twice the traffic without doubling your infrastructure bill? Whether you run hundreds of small microservices or one massive monolith, peak-time …
System Models, JEV — What is it?
As AI systems increasingly participate in core business workflows, a fundamental question emerges: how should humans and intelligent systems effectively collaborate? This session …
Lightning Talks
Demystifying Primitives in Patterns from a Java Programmer's Perspective
Pattern matching in Java has evolved rapidly from instanceof patterns to record patterns and switch expressions. With recent Java releases, support for primitive types in patterns …
Experimenting in Modern Java for Data Science and AI
For years, developers associated Java primarily with heavyweight enterprise applications and multi-module builds. Today, modern Java is remarkably nimble for fast experimentation, …
How AI Coding Agents Actually Work?
AI coding agents can inspect a repository, edit multiple files, run builds, diagnose failures, and revise their own changes. Yet the language model at their centre generates …
Project Valhalla: Value Objects, Identity, and Java’s Future
Java developers frequently model data using standard objects, even when that data has no meaningful identity. Project Valhalla bridges the historical divide between primitive and …
When AI Writes the Code, Who Verifies the Logic?
What happens when a non-developer uses AI to build a mission-critical financial verification engine, reverse-engineers transaction formats across 22 banks, and tests it against …
Talks
85% Leaner: Cutting the Hidden Cost of AI Agents
Speakers:
As organizations move AI agents into production, they encounter an unexpected bottleneck: token overhead and tool discovery latency. Standard Model Context Protocol (MCP) …
Building Smarter Enterprise AI Applications in Java: Agentic Patterns
Connecting an LLM to Spring Boot is straightforward. But running agents in production quickly hits real-world walls: exploding token bills, context window bloat, task forgetting, …
CausaAI: Java Root Cause Analysis with Agentic AI
When a Java application fails in production, finding the root cause is only half the battle. The harder question is: where in the code should we fix it, and what change should we …
Resilient AI Agents in Java: A Production Framework for When Reasoning Isn't Enough
AI agents fail in production in ways traditional services don’t: non-deterministic outputs, cascading tool failures, context window exhaustion, and hallucinations that pass …
Running LLMs Natively on the JVM
Every Java shop that adds an LLM feature seems to inherit a second runtime along with it — a Python sidecar running vLLM or TGI, its own deploy pipeline, its own on-call surface. …
The hidden JVM tax of running AI agents in Java
AI Agents built on the JVM look like microservices from the outside, but behave unlike anything you have operated before. Long-running LLM streams pin carrier threads, complex …
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