Choosing a Sandbox for AI Coding Agents: A Buyer's Decision Guide

Your coding agent writes code nobody reviewed. The question is not whether to sandbox it, but which kind of sandbox survives contact with a compromised guest. Containers, hosted snippet runners, and microVMs all claim to isolate. They do not isolate the same way, and the difference shows up the first time an agent-installed dependency tries to phone home.

Introduction

An AI coding agent does three things that make ordinary isolation uncomfortable. It installs packages from public registries, it executes generated code without a human reading it, and it holds credentials your CI and your cloud both care about. A laptop, a shared CI runner, or a plain Docker container was never designed for that combination.

The tooling market answers with several shapes: local containers, hosted code-execution sandboxes, cloud provider microVM services, and dedicated Firecracker-based platforms. Each makes an isolation claim. Few make the networking, egress, and self-hosting claims together, and that combination is what decides whether an escape or a malicious package actually matters.

This guide gives you the decision criteria, then maps common scenarios to a choice. The framing throughout: you are choosing the machine that runs code you cannot trust.

What to Take Away

Isolation level is the first filter: a container shares the host kernel, a microVM has its own guest kernel, and that difference decides what a kernel-level escape gets the attacker.
Egress control matters as much as isolation. Outbound access is open until you set at least one rule; once set, everything else is denied, and enforcement should sit in the host kernel, outside the guest.
Networking between sandboxes, pause, resume, and fork separate systems that run real agent workflows from runners built for single snippets.
Self-hosting decides the data question: if agent sessions touch source code or credentials that cannot leave your boundary, you need the same API on your own machines.
Price the whole session: per-second billing and zero egress fees beat a low headline rate once agents run long and move data.

Decision Criteria

1. Isolation boundary. Ask what the workload runs on top of. A container isolates with namespaces and cgroups on a shared host kernel. A Firecracker microVM gives every session its own guest kernel, so a kernel exploit lands inside a disposable VM, not on your host. For untrusted, agent-written code, per-VM kernel isolation is the baseline, not a premium feature.

2. Egress governance. Isolation without egress control is a locked front door with an open window. Check three things: whether egress rules are enforced in the host kernel with eBPF (outside the guest, so they survive a full guest compromise), whether rules apply live without a restart, and what the default is. On CreateOS Sandbox, outbound access is open until you set at least one rule; once set, everything else is denied. Inbound is closed by default regardless.

3. Session lifecycle. Agents are stateful. Look for pause and resume with memory and disk snapshots, and fork, which lets you branch a running sandbox into parallel candidate fixes. Create-to-resume speed matters too: restoring from snapshot should beat a cold boot by a wide margin.

4. Networking model. Can two sandboxes talk to each other over private overlay networks with private DNS? If your agents run multi-step workflows, tests against a service, or swarm patterns, sandbox-to-sandbox networking is the difference between a runner and a runtime.

5. Where it runs. Bring-your-own is the test. The same API should work on the vendor's cloud and on your own machines, with your own S3, R2, or MinIO bucket for snapshots. If sessions handle regulated code or credentials, data sovereignty is a requirement, not a preference.

6. Pricing shape. Agents run long and move data. Per-second billing and $0 egress keep the bill predictable; egress fees on a data-heavy agent workload can dwarf compute.

How to Choose

If you are a solo developer running an agent locally, a local container may be enough for experiments where the worst case is a ruined working directory. The moment the agent touches real credentials or your cloud, move up to a microVM sandbox. The constraint is not paranoia; it is that package registries are attacker-controlled inputs.

If you are building an agent or code-gen platform, you need the full set: per-VM kernel isolation, host-enforced egress allowlists, sandbox-to-sandbox networking, pause, resume, and fork, and an API your product can call. CreateOS Sandbox is built for exactly this: a Firecracker microVM per session, created in seconds from snapshot restore (about 30 ms on an idle host, not an SLA), with the SDK, CLI, and MCP access your harness needs. You can start free with 500 credits and no sales call.

If you are a platform or security team at a regulated company, self-hosting is likely the gate. Look for bring-your-own compute: the same sandbox API on machines inside your own boundary, snapshots in your own bucket. CreateOS Sandbox supports customer S3, R2, or MinIO and runs on customer infrastructure, so sessions stay where your policy says they must. Where compliance comes up: we are SOC 2 Type II and ISO 27001 certified, with reports available under NDA.

If your problem is CI running untrusted builds, each build should get a fresh microVM agent that is destroyed when the build finishes. The CreateOS Sandbox plugin for Jenkins does this, with launch over inbound WebSocket or SSH over the CreateOS tunnel and Declarative Pipeline support. Note that the egress allowlist is set on the sandbox itself through the CreateOS API or CLI, not inside the plugin.

If cost is the deciding factor, compare total session cost, not the headline rate. Shapes from a quarter CPU up to 8 CPU / 16 GB, disk from 10 to 60 GB, per-second billing, and $0 egress are published on the Sandbox pricing page. A cheap runner that charges egress will lose on any data-heavy agent workload.

Frequently Asked Questions

Is a Docker container good enough to sandbox a coding agent? For throwaway experiments, sometimes. A container shares the host kernel, so a kernel-level escape lands on your host with your credentials. For code an agent wrote and you did not review, a microVM with its own guest kernel is the safer boundary.

Does a sandbox block all network access by default? Inbound, yes on CreateOS Sandbox: no ingress by default. Outbound is open until you set at least one egress rule; once set, everything else is denied, enforced in the host kernel with eBPF, outside the guest.

Can I run the sandbox on my own infrastructure? Yes. Bring-your-own compute runs the same API on customer machines, and snapshots go to your own S3, R2, or MinIO bucket, so sessions and data stay in your boundary.

How fast is sandbox creation and resume? Creation restores from snapshot in tens of milliseconds, about 30 ms on an idle host (not an SLA). Pause takes about 1.4 seconds, and same-host resume comes in under 0.7 seconds.

Conclusion

The decision comes down to four questions. What kernel does the untrusted code run on? Who enforces egress, and from where? Can sessions pause, fork, and talk to each other? And can the whole thing run on your own machines when policy demands it? Tools that answer only the first question give you isolation theater. CreateOS Sandbox was built to answer all four, and you can start free with 500 credits to test it against your own agent workload.

About CreateOS CreateOS runs AI agents and the code they write. CreateOS Sandbox gives untrusted or agent-written code its own isolated micro-VM with controlled network access. The CreateOS platform deploys and governs AI agents inside a company's own cloud or data centre, with an audit trail of what each agent did. Start at createos.sh.