Flurin Gishamer
I am a Senior Data Scientist at Open Systems and the creator of Diskurs—an open-source framework for building multi-agent systems. My work spans natural language processing, large language models, and generative AI, and lately, I have been focusing mostly on multi-agent systems.
I enjoy learning new things, exchanging ideas with others, and sharing my discoveries in ways that spark meaningful dialogue.
Beiträge
This workshop will teach you how to use open-source AI models and frameworks to design and implement reliable multi-agent systems for real-world applications. We’ll begin by covering key concepts (including free-form interaction (chat) vs. workflow automation), then explore the architecture and implementation using the open-source framework diskurs, and conclude with an introduction to testing multi-agent systems. By the end, you’ll have the theoretical background and practical experience to develop robust multi-agent applications and choose the right open source AI models for your use case.
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AI-powered autonomous agents are changing the way we interact with large language models (LLMs). In this hands-on workshop, you will learn how to build and customize AI agents using open-source models and frameworks. We will explore how these agents reason, plan, and execute tasks, and how they can be integrated with external tools or run using local models (e.g., via LM Studio or ollama).
This workshop is designed for developers, researchers, and AI enthusiasts who want to build intelligent, privacy-friendly LLM applications. No prior experience with AI agents is required—just basic Python knowledge and a curiosity to experiment!
--- What You’ll Learn ---
✅ What AI agents are and how they interact with LLMs
✅ How to build AI-driven workflows using open-source frameworks
✅ How to run agents on local language models for privacy-friendly solutions
✅ How to integrate external tools and APIs into agent reasoning
✅ Bonus: If there’s interest, we can touch on synthetic data generation as a tool for testing and evaluation