JavaConference50min
Empowering Agentic AI with Industrial and Scientific JavaFX Desktop Applications via MCP
This talk demonstrates how to integrate JavaFX desktop applications with Agentic AI using the Model Context Protocol (MCP), enabling advanced automation, visualization, simulation, and AI-driven interfaces. It covers real-world examples, integration challenges, and local inference, providing actionable insights for developers and decision makers seeking to enhance existing tools with AI.
Michael HofferHamilton Freiburg GmbH
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Thursday, October 9, 09:30-10:20
BOF 2
Most companies, research groups and development temas have established, high-quality desktop tools that are cut off from the benefits of Agentic AI. This talk will show how to integrate these existing JavaFX applications with AI using the Model Context Protocol (MCP), unlocking sophisticated new automation workflows.
We will explore applications that enable AI to:
- Interpret complex datasets through visualization and image processing tools.
- Test control strategies and learn from feedback in interactive simulation systems.
- Understand and optimize algorithms using specialized coding environments for cache optimization in simplified C code.
- Visually communicate and perform tasks through augmented and specialized user interfaces with advanced AI/human collaboration features.
This session will demonstrate these integrations with real-world examples, address the challenges involved, and discuss local inference options like LM-Studio. You will leave with actionable insights on how to transform your existing desktop applications into powerful assets for agentic AI.
This talk is targeted at developers and decision makers who want to understand how to leverage their existing applications, tools and domain knowledge to bringing AI into their workflows.
We will explore applications that enable AI to:
- Interpret complex datasets through visualization and image processing tools.
- Test control strategies and learn from feedback in interactive simulation systems.
- Understand and optimize algorithms using specialized coding environments for cache optimization in simplified C code.
- Visually communicate and perform tasks through augmented and specialized user interfaces with advanced AI/human collaboration features.
This session will demonstrate these integrations with real-world examples, address the challenges involved, and discuss local inference options like LM-Studio. You will leave with actionable insights on how to transform your existing desktop applications into powerful assets for agentic AI.
This talk is targeted at developers and decision makers who want to understand how to leverage their existing applications, tools and domain knowledge to bringing AI into their workflows.
Michael Hoffer
As Head of Software Development at Hamilton Freiburg GmbH, Michael leads a team of software developers and embedded engineers specialized in laboratory automation systems and liquid handling robots. He received the JavaOne Rock Star award in 2014 and 2015 and was a regular speaker at prominent Java-related conferences. He developed VRL-Studio, an innovative visual programming environment that integrates visual and text-based programming, and VMF, a modern modeling framework for Java used in developing complex modular systems for demanding industrial and research applications. His research during his PhD and postdoc work focused on visual programming concepts and meta-programming techniques for modeling and simulating highly complex physical processes. As part of his current work, he is creating a variety of industrial-grade ML/AI-based software solutions. Michael aims to create a visual general-purpose programming language for the Java platform that seamlessly integrates with Agentic AI and microservice architectures.
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