Microsoft Reactor

Brian Benz: Seamless AI Integration for Java: From Exploration to Deployment
Curious how to embed advanced AI functionality into your Java applications without refactoring? In this session, we'll walk through experimenting with cutting-edge models to deploying production-ready solutions at scale, leveraging LLMs, GitHub Copilot, GitHub Models, Model Context Protocol (MCP) and more in your favorite Java IDEs.

Julien Dubois: Building an AI agent with Structured Outputs, Function Calling, and MCP

AI agents are programs that can act autonomously: to do this, they must be able to communicate programmatically with an LLM and perform actions.

In this session, we will explore:

• Structured Outputs: how to require an LLM to respond following a JSON schema, allowing to map the result to Java objects
• Function Calling: how to define and call Java functions from within an AI model
• MCP: the new protocol that standardizes how LLMs communicate with different data sources and tools

We will use the code, demos, and documentation I created to implement these features in LangChain4j using OpenAI's brand-new Java SDK.