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