Provisioning for the Agentic Era: How Databricks Built a Self-Serve Infrastructure Vending Machine

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 internally — for the agentic era and beyond.