Introducing the Premium Track AI

The Premium AI Track is for Java teams who need to integrate AI into real systems—not experiments. It focuses on AI-assisted coding that remains understandable and maintainable, with concrete habits that prevent technical debt and loss of ownership. You’ll learn how to use AI tools responsibly, including running models locally to control cost, latency, and sensitive data.

Beyond daily coding, the track addresses the architectural impact of AI. Sessions cover clean architecture for AI-heavy systems, evolving design patterns, and practical approaches to agentic and multi-agent systems using Java and Jakarta EE. The track concludes with enterprise integration and operational guidance, helping teams introduce AI into existing platforms while managing cost, sustainability, and compliance—so AI becomes a controlled capability, not a long-term risk.

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JCON 3 Day Premium Pass

EUR 799.00

Our VIP-Premium-Pass.

This is our All-in Ticket. Come to our VIP-Dinner, get access to our Premium AI Track, experience JCON in it's full glory!

  • VIP-Status
  • Premium AI Track
  • Badge ready to pick up at our fast lane
  • 1x Lunch Voucher per Day
  • 5x Softdrink Vouchers per Day
  • 5x Coffee Specialty Vouchers per Day
  • Access to all standard conference sessions
  • Free Snacks
  • Q&A with Speakers
  • Access to all recorded sessions
Go All-In

 Featured Sessions

Local Development in the AI Era

Local development gives teams control over dependencies, costs, and reproducibility. AI-assisted tooling often breaks that model by relying on always-on cloud services and opaque integrations. This session shows how to keep development local while still benefiting from AI.

You’ll see how to:

  • Run and evaluate open-source AI models locally
  • Connect local models to AI-powered code assistants
  • Apply agentic AI patterns in locally running applications
  • Compare local and remote models to make informed trade-offs around performance, accuracy, cost, and privacy

The focus is on practical setups and decision criteria, not hype. You’ll leave with concrete guidance on when local AI makes sense, when it doesn’t, and how to integrate it responsibly into modern Java-based development workflows.

Kevin Dubois

Senior Principal Developer Advocate at IBM

Seven Habits of Highly Effective AI Java Coding

AI code assistants can speed up Java development—but without the right habits, they also create confusion and technical debt.

This session shows how to work effectively with AI in real Java projects. It focuses on practical habits and mindset shifts that help you evaluate AI-generated code, integrate it safely into your workflow, and maintain code quality.

The key principle is simple: you own the code—no matter who wrote it. Learn how to move faster with AI without sacrificing readability, maintainability, or confidence in what you ship.

Ideal for: Java developers using (or planning to use) AI coding assistants in production code.

Jonathan Vila

Developer Advocate at Sonar

Design Patterns for Multi-Agent Systems

As AI systems move beyond single prompts, teams must choose how intelligence is orchestrated: inside one model (mixture-of-experts), across collaborating agents, or through explicit workflows and task graphs.

This session compares these three approaches by implementing the same task in each style, using concrete patterns such as routing, planner–critic loops, shared memory, and tool use. Rather than theory, the focus is on engineering trade-offs—latency, cost, reliability, and maintainability.

Attendees leave with a practical decision framework they can apply immediately when designing or evolving production AI systems.

Brian Sam-Bodden

Principal Applied AI Engineer at Redis