Greg Wood

As Databricks' go-to-market organization grew past 7,000 people — and AI agents became first-class consumers of platform resources alongside human engineers — our field team ran into a familiar problem: shared demo environments couldn't keep up. Too many users needed admin-level access, platform limits became real constraints, and it was hard to see who was using what, whether that "who" was a person or an agent. In this session, we'll share how we addressed that by building the Field Engineering Vending Machine (FEVM), a Databricks App that gives every engineer — and every agent — just-in-time, isolated, and governed environments on demand. Instead of filing tickets or juggling shared workspaces, engineers describe what they're trying to do — build a demo, run a hackathon, reproduce an issue, spin up an agentic workflow — and FEVM delivers a fit-for-purpose environment in minutes, then cleans it up automatically when it's no longer needed. We'll show how this "vending machine" model has improved speed, control, and cost visibility for thousands of users and countless agent runs, and why building it as a Databricks App has become a blueprint for how we dogfood the platform…

AI for enterprises, particularly in the era of GenAI, requires rapid experimentation and the ability to productionize models and agents quickly and at scale. Compliance, resilience and commercial flexibility drive the need to serve models across regions. As cloud providers struggle with rising demand for GPUs in environments, VM shortages have become commonplace, and add to the pressure of general cloud outages. Enterprises that can quickly leverage GPU capacity in other cloud regions will be better equipped to capitalize on the promise of AI, while staying flexible to serve distinct user bases and complying with regulations. In this presentation we will show and discuss how to implement AI deployments across cloud regions, deploying a model across regions and using a load balancer to determine where to best route a user request.