Brian Benz
Microsoft
LEARN, CONNECT, BUILD
Microsoft Reactor
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Ready to get started with AI and the latest technologies? Microsoft Reactor provides events, training, and community resources to help developers, entrepreneurs and startups build on AI technology and more. Join us!
LEARN, CONNECT, BUILD
Microsoft Reactor
Join Microsoft Reactor and engage with developers, entrepreneurs, and startups live
Ready to get started with AI and the latest technologies? Microsoft Reactor provides events, training, and community resources to help developers, entrepreneurs and startups build on AI technology and more. Join us!
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Seattle Java User Group Meetup - May
23 May, 2025 | 1:00 AM - 3:00 AM (UTC) Coordinated Universal Time
Location: Redmond
Address: Redmond Reactor 3709 157th Ave NE, Redmond, WA 98052
- Format:
In person (Redmond)
Livestream
Topic: Coding, Languages, and Frameworks
Language: English
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.
Speakers
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Julien Dubois
Microsoft
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