In this session, we'll explore design patterns for developing scalable multi-agent applications. We'll focus on leveraging Azure Cosmos DB as the single database to manage agent transactional data, operational application data, vector embeddings, text data, and more! We'll see why Azure Cosmos DB is the database of choice for any-scale AI apps.

Key topics include:

Agent-to-Agent Transactions: Discover methods to facilitate seamless and efficient transactions between agents, ensuring data consistency and reliability.

Statefulness: Learn how to maintain state across interactions, enabling agents to remember and build upon previous exchanges.

Chat History: Explore techniques for storing and retrieving chat histories, allowing agents to provide contextually relevant responses.

Retrieval-Augmented Generation (RAG): Understand how to implement patterns like vector Search with DiskANN for low-latency, highly scalable vector search, and Hybrid Search to enhance the relevancy of agent contexts and responses.

Function Calling: Learn how to enable structured queries from Cosmos DB to expand scenarios beyond simple RAG approaches.

Multitenancy: See how to support multitenant applications, ensuring data isolation and security for different users or clients.

Scalability: Examine features that handle large volumes of data and high-throughput workloads with Azure Cosmos DB's Dynamic Autoscale, which is essential for multi-agent systems.