Build a multi-agent application leveraging MCP (Model Context Protocol) with the Microsoft Agent Framework in C# or LangGraph in Python, integrated with Azure Cosmos DB for scalable and high-performance data persistence and retrieval. Define agents, functions, and external service integrations, implement memory, state management, and semantic search using Azure Cosmos DB. By the end, you’ll have a robust AI agent system designed for real-world applications.

Please RSVP and arrive at least 5 minutes before the start time, at which point remaining spaces are open to standby attendees.

Build a multi-agent application leveraging MCP (Model Context Protocol) with the Microsoft Agent Framework in C# or LangGraph in Python, integrated with Azure Cosmos DB for scalable and high-performance data persistence and retrieval. Define agents, functions, and external service integrations, implement memory, state management, and semantic search using Azure Cosmos DB. By the end, you’ll have a robust AI agent system designed for real-world applications.

Please RSVP and arrive at least 5 minutes before the start time, at which point remaining spaces are open to standby attendees.

The use of multiple Large Language Models (LLMs) working together perform complex tasks, known as multi-agent systems, has gained significant traction. While orchestration frameworks like LangGraph and Semantic Kernel can streamline orchestration and coordination among agents, developing large-scale, production-grade systems can bring a host of data challenges. Issues such as supporting multi-tenancy, preserving transactional integrity and state, and managing reliable asynchronous function calls while scaling efficiently can be difficult to navigate.

Leveraging insights from practical experiences in the Azure Cosmos DB engineering team, this talk will guide you through key considerations and best practices for storing, managing, and leveraging data in multi-agent applications at any scale. You’ll learn how to understand core multi-agent concepts and architectures, manage statefulness and conversation histories, personalize agents through retrieval-augmented generation (RAG), and effectively integrate APIs and function calls.

Aimed at developers, architects, and data scientists at all skill levels, this session will show you how to take your multi-agent systems from the lab to full-scale production deployments, ready to solve real-world problems. We’ll also walk through code implementations that can be quickly and easily put into practice, all in Python.