Andrew Sellers

Learn how a Streamhouse data architecture powers agents, applications, and lakehouses with the always current state of your business. We'll dive into real-world examples from Together AI, Accenture, and Indeed, along with demos and new product launches that leave you with a practical approach to solving the toughest data challenges.

Autonomous agents are reshaping enterprise operations, but scaling them isn’t just about smarter AI—it’s about better infrastructure. Agents need real-time data, seamless tool integration, and shared outputs across systems. Rigid request/response models create bottlenecks, while event-driven architecture (EDA) unlocks the flexibility and scalability agents require. This session will show how EDA enables autonomous agents to thrive. Key takeaways include: - How EDA enables real-time, adaptive agent workflows and multi-agent problem solving. - Key design patterns like Orchestrator-Worker, Multi-Agent Collaboration, and Market-Based Competition. - Strategies for leveraging Kafka to handle scalability, fault tolerance, and low latency. - Lessons from microservices evolution to solve interoperability and context-sharing challenges. This talk is for engineers and architects building scalable AI systems. You’ll leave with actionable insights to design resilient, event-driven agents and future-proof your infrastructure for enterprise-scale AI.

Join Deloitte, AWS, Confluent, and MongoDB for a dynamic panel discussion that tackles a pivotal question: Is your data truly ready for trustworthy AI? From machine learning to generative AI, discover the challenges and opportunities of making data AI-ready for enterprises.