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Your AI agent just wrote code. Where do you run it — safely? Would you run code you didn't write on a shared kernel — with the same access to your files, network, and credentials that you have? For AI-generated code, that's a boundary you may not want to bet on.
AWS Lambda MicroVMs give every workload its own Firecracker virtual machine: hardware-level isolation, its own kernel and full OS, near-instant launch from a snapshot, and no compute charges while suspended. The same pattern secures any untrusted code — a contributor's CI job, a per-tenant SaaS workload — but it starts with the one you came for.
In this free, hands-on workshop you'll build that sandbox yourself and leave with patterns you can apply to your own systems.
Who this is for: engineers building AI-agent execution, CI/CD, or multi-tenant platforms who need real isolation without managing servers. Comfortable in the AWS Console and a CLI, familiar with serverless basics.
What you'll build
You start with a foundation module, then choose the use cases that fit your work — they're independent, so you can do one, two, or all three.
Then pick whichever apply:
You'll leave knowing when to reach for a MicroVM over a container or regular Lambda, and with the core building blocks — images, snapshots, network connectors, secrets handling, and the launch/suspend/resume/terminate lifecycle — to apply it to your own systems.