As AI systems evolve, the need for robust infrastructure increases. Enter Dapr Agents: an open-source framework for creating production-grade AI agent systems. Built on top of the Dapr framework, Dapr Agents empowers developers to build intelligent agents capable of collaborating in complex workflows - leveraging Large Language Models (LLMs), durable state, built-in observability, and resilient execution patterns. This workshop will walk through the framework’s core components and through practical examples demonstrate how it solves real-world challenges.

Sonification–using sound to represent data–is a niche technique for exploring complex patterns, expanding the sensory dimensions of data analysis, and discovering musical ideas that are otherwise inaccessible.

In this hands-on session, participants will learn the ins and outs of building sonification pipelines through practical examples with data from healthcare and physics. We’ll also cover key software design considerations for creating flexible and expressive systems that map data into sound. Whether you're a developer, data scientist, researcher, educator, or artist, this session will help you listen to your data.

Large Language Models (LLMs) are unlocking transformative capabilities — but integrating them into complex, real-world applications remains a major challenge. Simple prompting isn’t enough when dynamic interaction with tools, structured data, and live context is required. This workshop introduces the Model Context Protocol (MCP), an emerging open standard designed to simplify and standardise this integration. Aimed at forward-thinking developers and technologists, this hands-on session will equip participants with practical skills to build intelligent, modular, and extensible LLM-native applications using MCP.

Graph-based Retrieval-Augmented Generation (GraphRAG) enhances large language models (LLMs) by grounding their responses in structured knowledge graphs, offering more accurate, domain-specific, and explainable outputs. However, many of the graphs used in these pipelines are automatically generated or loosely assembled, and often lack the semantic structure, consistency, and clarity required for reliable grounding. The result is misleading retrieval, vague or incomplete answers, and hallucinations that are difficult to trace or fix.

This hands-on tutorial introduces a practical approach to evaluating and improving knowledge graph quality in GraphRAG applications. We’ll explore common failure patterns, walk through real-world examples, and share a reusable checklist of features that make a graph “AI-ready.” Participants will learn methods for identifying gaps, inconsistencies, and modeling issues that prevent knowledge graphs from effectively supporting LLMs, and apply simple fixes to improve grounding and retrieval performance in their own projects.

As AI-powered agents and workflows grow in complexity, understanding their internal behavior becomes critical. In this hands-on workshop, you’ll build an agent and explore how observability tooling in PyCharm can help you trace, inspect, and debug its behavior at every stage – without having to leave the IDE.

Graph databases give the same importance to relationships as they do to data. Knowledge graphs allows you to uncover insights in your data and efficiently explore the relationships in your data.

A workshop session to show you the basics on how to use Docling to enhance document ingestion in your AI workflow.

Retrieval-Augmented Generation (RAG) systems rely heavily on the quality of the retrieval process to generate accurate and contextually relevant outputs. In this 90-minute tutorial, we explore practical techniques to enhance retrieval across three key stages: pre-retrieval, mid-retrieval, and post-retrieval. Participants will learn how to optimize data preparation, query strategies, reranking, and evaluation to significantly improve the performance of RAG systems. A real-world case study will guide attendees through implementing these methods in a complete retrieval workflow.