Sathish Gangichetty

Agentic commerce could drive up to $1T in retail revenue by 2030, but the real opportunity spans every industry. Most agentic systems today are still reactive, relying on chat prompts. True transformation requires always-on, long-running agents that monitor data, reason through exceptions, and act autonomously.In this session, we’ll walk through how to build asynchronous, event-driven agentic systems on Databricks using a supply chain example. Learn how to move beyond chatbots to resilient, system-to-system workflows by orchestrating Structured Streaming, Model Serving, Lakebase, and Agent Bricks.Key takeaways:• A scalable design pattern for long-running, event-driven agents• Always-on anomaly detection with streaming + ML• Stateful task queuing and high-concurrency routing with Lakebase• Multi-step agent reasoning with tools and safe execution• Evaluation and human-in-the-loop escalation for edge cases

Building an app has never been easier. Deploying an enterprise app— governed, scalable, connected to real-time data— has never been harder. Store associates, restaurant managers, and operations teams make thousands of decisions a day— but the apps they rely on are slow to build, disconnected from analytics, and starved of real-time context. The reason? A wall between analytical and operational systems that forces teams to maintain fragile ETL pipelines, over-provision databases, and wait months for production-ready tools. Lakebase tears that wall down. Databricks' serverless Postgres eliminates the infrastructure overhead, the ETL pipelines, and the DBA bottleneck that have kept intelligent apps out of frontline hands. In this session, we'll show what it means to build AI-first applications for consumer industries, walking through the end-to-end process: from use case design to live deployment, including a live app build on stage. Hear from brands already shipping Lakebase-powered apps, and leave with a repeatable framework for turning lakehouse intelligence into the tools your frontline teams actually use.