4:00 p. m. - 5:00 p. m. (UTC)

The Azure AI Agent Service is a seamless blend of service and SDK that simplifies the development of robust AI-driven solutions. In this session, you'll learn how to build your own code-first AI agent with Azure that can answer questions, perform data analysis, and integrate external data sources. You'll also explore more complex architectures, including multiple agents working together.

10:00 p. m. - 11:00 p. m. (UTC)

The Azure AI Agent Service is a seamless blend of service and SDK that simplifies the development of robust AI-driven solutions. In this session, you'll learn how to build your own code-first AI agent with Azure and Python that can answer questions, perform data analysis, and integrate external data sources. You'll also explore more complex architectures, including multiple agents working together. Follow along: Create agentic AI solutions by using Azure AI Foundry Practical Foundation for AI Agents: A Developer's Guide on Azure AI Foundry, Apps, and Data

7:00 p. m. - 8:00 p. m. (UTC)

Discover the power of multi-agent AI systems through live demonstrations and hands-on learning with patterns including group-chat, reflection, selector, and swarm. Harness the Semantic Kernel Process Framework to automate and scale critical business processes, from customer support to project management using Python. Follow along: Create agentic AI solutions by using Azure AI Foundry Practical Foundation for AI Agents: A Developer's Guide on Azure AI Foundry, Apps, and Data

10:00 p. m. - 11:00 p. m. (UTC)

Thanks to GitHub Models, all you need to build your first AI Agent is a GitHub account! GitHub Models includes powerful models like OpenAI gpt-4o, DeepSeek-R1, Llama-3.1, and many more, ready to try out in the playground or in your code. In this session, we'll demonstrate how to connect to GitHub Models from Python, and then build agents using popular Python packages like PydanticAI, AutoGen, and Semantic Kernel. You can follow along live in GitHub Codespaces, or try the examples yourself anytime after the session.

7:00 p. m. - 8:00 p. m. (UTC)

The rise of multi-agent AI applications is transforming how we build intelligent systems - but how do you architect them for real-world scalability and performance? In this session, we’ll take a deep dive into a production-grade multi-agent application built with LangGraph for agent orchestration, FastAPI for an API layer, and Azure Cosmos DB as the backbone for state management, vector storage, and transactional data. Through a detailed code walkthrough, you’ll see how to design and implement an agent-driven workflow that seamlessly integrates retrieval-augmented generation (RAG), memory persistence, and dynamic state transitions. We’ll cover: Agent collaboration with LangGraph for structured reasoning Real-time chat history storage using Azure Cosmos DB - the same database that powers the chat history in ChatGPT, the fastest-growing AI agent application in history Vector search for knowledge retrieval with Cosmos DB's native embeddings support FastAPI’s async capabilities to keep interactions responsive and scalable By the end of this session, you’ll have a clear blueprint for building and deploying your own scalable, cloud-native multi-agent applications that harness the power of modern AI and cloud infrastructure. Whether you're an AI engineer, cloud architect, or Python developer, this talk will equip you with practical insights and battle-tested patterns to build the next generation of AI-powered applications

10:00 p. m. - 11:00 p. m. (UTC)

Retrieval Augmented Generation (RAG) is a popular technique to get LLMs to answer questions based off your own knowledge base. "Agentic RAG" adds planning, tool use, and reflection to a RAG flow. In this session, we'll demonstrate an Agentic RAG workflow using OpenAI Function Calling with NL2SQL for structured data, Azure AI Search for unstructured data, and Bing Search API for live web search. Follow along: Create agentic AI solutions by using Azure AI Foundry Practical Foundation for AI Agents: A Developer's Guide on Azure AI Foundry, Apps, and Data

4:00 p. m. - 5:00 p. m. (UTC)

By giving your agents access to a code interpreter, they instantly become more capable. They can solve math and logic problems, query CSVs and spreadsheets, make graphs, and more. We'll learn about how a AI agents use code interpreters, how to safely use a code interpreter, and how to add a secure and scalable code interpreter to AI agents in just a few lines of code using Azure Container App dynamic sessions.

10:00 p. m. - 11:00 p. m. (UTC)

Join us for an in-depth technical session showcasing Contoso Outdoor's cutting-edge Agentic AI System, designed to redefine customer service interactions. This presentation is geared towards developers interested in the practical implementation of AI technologies in customer support. We'll kick off with a live demo of our AI customer service agent, highlighting its real-time voice mode capabilities. At the heart of this feature is our robust API, which enables seamless, instantaneous communication between the AI agent and customers. Witness how our real-time API handles voice data, allowing the AI to deliver rapid and context-aware responses that enhance the user experience. Our session will break down the system architecture, focusing on the integration of a JavaScript React front-end with a Python FastAPI backend. Participants will get a raw look at the code, providing valuable insights into the development process on both ends of the system. Additionally, we'll cover the technical aspects of working with audio in AI, discussing how multiple AI agents are used effectively through prompting techniques to enhance service delivery. This session is a must-attend for developers looking to understand the intricacies of real-time APIs in multi-agent systems and gain practical knowledge that can be applied to their own projects. Follow along with the content: Find the best model for your generative AI solution with Azure AI Foundry

1:00 p. m. - 2:00 p. m. (UTC)

Join me for an exciting session on building intelligent multi-agent AI applications using Prompty and Azure AI Foundry. We'll start with an overview of Azure AI Foundry and Prompty, and explore how agent systems are designed, and deployed. Next, I’ll guide you through a hands-on project demo showing these tools and components in action. You’ll see how agents use knowledge and available tools to perform actions based on user query. Along the way, I’ll share practical tips on handling tricky error scenarios, debugging, and evaluating your AI systems effectively. https://aka.ms/CreateAgenticAISolutions https://aka.ms/ai_agents_apps__data

10:00 p. m. - 11:00 p. m. (UTC)

Our VoiceRAG solution combines Azure OpenAI realtime audio models with Azure AI Search to create an experience that lets users talk realtime with your data. In this session, we'll dive into the architecture: a frontend in JavaScript that uses websockets to send live audio back and forth, and a backend in Python that uses function calling to empower the LLM with retrieval and grounding capability. The same solution can be used to develop real-time agent solutions, like for retail purchasing, education, and customer support. Follow along: Create agentic AI solutions by using Azure AI Foundry Practical Foundation for AI Agents: A Developer's Guide on Azure AI Foundry, Apps, and Data

4:00 p. m. - 5:00 p. m. (UTC)

The application showcases a shipping company where agents manage shipments, customers and product informations. The main goal of this repository is to illustrate how easy it is to have agents not just reading data but also acting on them. It extends the "Chat With Your Data" to "Chat and Act on Your Data.

7:00 p. m. - 8:00 p. m. (UTC)

Retrieval Augmented Generation (RAG) is a popular technique to get LLMs to answer questions based off your own knowledge base. "Agentic RAG" adds planning, tool use, and reflection to a RAG flow. In this session, we'll extend our most popular open-source RAG solution with a reflection step, prompting the LLM to realize whether it needs to repeat the retrieval in order to improve the answer quality.