As organizations race to integrate AI into their applications, the demand for systems that offer scalability, high availability, global distribution, and low latency has never been greater. Distributed SQL databases are emerging as a powerful solution, providing not only the performance required by modern, AI-driven workloads, but also the flexibility of SQL and strong consistency guarantees.

However, deploying these systems brings unique challenges. The familiar trade-offs between consistency, availability, and partition tolerance (the CAP theorem) still apply. Accessing data across geographies introduces network latency, and evolving data residency regulations now require thoughtful data placement and replication. Achieving rapid, automated recovery in the face of hardware, software, or network failures adds another layer of complexity—especially as AI and agent-based systems increase demands on reliability and performance.

In this talk, we’ll examine the inner workings of distributed databases, with a focus on best practices for designing applications that leverage their full capabilities—including for AI and agentic scenarios. Using Oracle Globally Distributed Database and other distributed SQL platforms, we’ll explore core architecture, advanced features, and the distinct application challenges of distributed data management. Patterns like sharding, data distribution, and parallel distributed transactions will be covered as practical ways to address latency and consistency—crucial for both traditional and AI-powered workloads.

To bring these concepts to life, we’ll share real-world case studies ranging from traditional applications to AI-driven and agent-based systems. By the end of the session, you’ll walk away with practical guidance and insights for designing scalable, resilient, and cloud-native solutions built for the next generation of AI-enabled workloads.