Build Fast, Stay Safe: Sandbox Architecture for AI Workloads

You have an idea for an AI agent, a new RAG pipeline, or some other experiment, but getting a safe environment to build it takes longer than the experiment itself. Between account provisioning, IAM policy reviews, and the fear of runaway costs, most experiments never leave the whiteboard. In this session, you’ll learn how teams set up isolated, disposable experiment environments with guardrails built in from day one, using production-ready, fully supported open-source patterns on AWS. We’ll walk through the architecture behind self-service sandboxes: how service control policies, identity boundaries, and budget automation work together to give builders freedom without risk. Then we’ll apply these patterns to AI workloads, where foundation model costs and compute sprawl make governance even more critical. Whether you’re a developer who just wants a safe place to break things, or an architect designing your team’s experimentation workflow, you’ll leave with practical patterns you can take back and implement.