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Use a container when you are testing the software an AI agent produces and a shared-kernel boundary is acceptable. Use a microVM when the agent itself is less trusted and a separate kernel is worth the startup and resource cost. A Docker Sandbox is Docker’s microVM-based option: it supplies a private Docker Engine inside the VM, so an agent can build and run containers without using the host daemon.
The choice is not only “container or VM.” Workspace mounts, network policy, credentials, host integrations, state persistence, and the workload being measured can change the result as much as the virtualization layer. Treat each as an explicit part of the evaluation design.
What are you actually comparing?
These terms describe different layers of isolation. A container is an application packaging and runtime unit. A microVM is a lightweight virtual machine with its own kernel. A Docker Sandbox is a managed way to run an autonomous agent inside a microVM while giving it a Docker daemon of its own.
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|---|---|---|---|
| Ordinary container | Shares the host kernel through namespaces and cgroups | Whatever daemon or socket you deliberately expose | Testing application behavior when the host-kernel boundary is acceptable |
| Standalone microVM | Boots its own guest kernel behind a hypervisor | Depends on the image and services you install | Testing less-trusted agent code with a stronger boundary from the host |
| Docker Sandbox | Runs each Sandbox in a microVM with its own kernel | A Docker Engine inside that Sandbox microVM | Letting an agent install packages and build or run nested containers without mounting the host Docker socket |
Docker describes containers and Sandboxes as complementary: containers package and run the application, while Sandboxes isolate autonomous agents. That distinction matters in an evaluation. You may use a container to run the product under test and a Sandbox or other microVM to contain the agent that writes, tests, or deploys it.
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Is a Docker Sandbox just a container?
No. Docker’s comparison page asks, “Is a Docker Sandbox just a container?” Its answer is that a container shares the machine’s kernel, while a Sandbox is a microVM with its own kernel. The hypervisor boundary therefore separates the agent from the host kernel in a way an ordinary container does not.
A container still has meaningful controls. Namespaces separate processes, mounts, users, and networking views, while cgroups limit and account for resources. Those controls are often sufficient for cooperative or moderately untrusted workloads, but a kernel vulnerability or a dangerous host integration can turn a container escape into a host compromise. A microVM reduces the amount of host kernel exposed to the guest, but it does not make a malicious agent harmless or remove every path to the host.
How does each boundary affect an AI coding-agent evaluation?
Containers: fast application-level isolation
Use a container when the question is primarily “Does the generated software work?” Containers are convenient for repeatable dependencies, image-based fixtures, and ordinary build pipelines. They usually start with less work than a VM because no guest kernel has to boot.
The trade-off is that the agent and its processes ultimately rely on the host kernel. Exposing the host Docker socket gives the agent an especially powerful control path: Docker commands then operate through the host daemon, subject to that daemon’s permissions and policy. If the agent only needs to run tests inside a prebuilt container and cannot reach sensitive host interfaces, this may be an acceptable evaluation boundary. If it must be allowed to experiment freely, install arbitrary tools, or build nested images, treat the shared kernel and every mounted socket as part of the threat model.
MicroVMs: a separate guest kernel
A microVM is a small virtual machine designed to keep the operational overhead lower than a traditional general-purpose VM while retaining a guest kernel. Docker’s local Sandbox documentation identifies the hypervisor boundary as the key isolation control and says the virtual-machine monitor calls each platform’s hypervisor directly.
This is useful when the agent may execute hostile or simply unpredictable code and you want a kernel boundary between that code and the host. It is not an absolute security guarantee: network routes, shared files, credentials, device exposure, management APIs, and guest vulnerabilities still matter. A microVM is a boundary to test and configure, not a substitute for least privilege.
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Docker Sandboxes: microVM isolation plus a private Docker Engine
For agents that need Docker build, run, or Compose workflows, Docker documents a separate Docker Engine inside each local Sandbox microVM. The agent can invoke Docker commands against that in-VM engine without a mounted host Docker socket and without using the host daemon.
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Which workspace mode should an evaluation use?
Choose the workspace mode to match the behavior you want to measure. Direct, clone, and mountless modes produce different security and reproducibility results, so record the mode in every evaluation report.
| Mode | What the agent can see and change | Use it when | Main caveat |
|---|---|---|---|
| Direct workspace | The host working tree is mounted read-write; edits are immediately visible on the host | You are testing an agent’s ability to modify the real checkout or participate in a live developer workflow | A mistaken command or malicious process can alter host files in the mounted tree |
| Clone workspace | The source is mounted read-only and the agent works in a private in-VM clone | You need the agent to edit a realistic project while preserving the host checkout | Results must be copied or exported deliberately, and the clone can diverge from later host changes |
| Mountless | No host workspace is provided | You want to test package installation, code generation from supplied inputs, or autonomous behavior without source access | The agent cannot inspect or modify the host project unless you provide data through another channel |
Do not compare a direct-mount run with a mountless run as if they were the same experiment. The available files, write permissions, and opportunity for data exfiltration are different.
Can an AI agent build Docker containers safely in a Sandbox?
A Sandbox removes one high-risk shortcut: the agent does not need the host Docker socket to build or run containers. Its Docker commands go to the private Engine inside the microVM. That makes nested builds a reasonable test condition when the agent must create images, start services, or exercise Compose-style workflows.
“Safely” still depends on the rest of the configuration. Images built by the agent can consume guest CPU, memory, disk, and network. Build steps can attempt to contact external registries, download tools, scan mounted files, or probe services reachable through the Sandbox network policy. A private daemon protects the host daemon, not every resource that the guest can access.
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- Decide whether the agent may push images or contact registries; allow only the destinations required by the test.
- Use a disposable Sandbox for untrusted build scripts and remove it when the run is complete if you do not need its state.
- Inspect build contexts for credentials and unrelated source files before supplying them.
What crosses the Sandbox boundary?
Docker’s isolation documents describe several deliberate connections. “Isolated” therefore means “isolated according to the configured policy,” not “nothing is shared.”
Network access
Outbound TCP is sent through a host proxy that enforces network policy. UDP is disabled by default; experimental UDP egress can be enabled under policy. ICMP is blocked. Record the active rules and allowed destinations, because package mirrors, model endpoints, source-control services, and registries can materially change an agent’s capabilities.
An allowlist is usually easier to reason about than unrestricted egress. Start with no destinations, add the package and service endpoints required by the workload, and test that a deliberately disallowed destination fails. If your application requires UDP, document that exception and treat it as a different security condition from the default configuration.
Credentials
Docker says the host-side proxy can inject credentials into outbound HTTP requests so credential values remain outside the VM. This reduces the chance that the agent can read the secret value directly, but it does not prevent the agent from using an authorized endpoint. Scope credentials to the smallest repository, registry, or API operation needed for the test and log which credential policy was active.
Host integrations and MCP servers
Supported configurations can share a skills store. Local stdio MCP servers run on the host outside the VM; Docker identifies this as an explicit host integration. Treat each such server as a trusted component. Its process, file permissions, returned data, and network behavior are part of the evaluation boundary, even if the agent itself runs in the Sandbox.
How should you choose a boundary for a particular threat model?
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Choose a container when the host is already the trust boundary
A container is a sensible default for benign generated code, fixed test suites, or performance-sensitive loops where the agent cannot reach sensitive host interfaces. Keep the container unprivileged, avoid unnecessary host mounts, and do not expose a Docker socket merely for convenience.
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Use a microVM when the agent may execute arbitrary shell commands, install unknown packages, inspect hostile repositories, or attempt kernel- and device-level probing. The separate kernel gives you a stronger starting boundary, provided you also restrict files, network, credentials, and host integrations.
Choose a Docker Sandbox when nested Docker is part of the task
A Docker Sandbox is the most direct fit when the agent must build and run containers but should not control the host daemon. It combines the microVM boundary with an in-VM Docker Engine, reducing the need to design and maintain that nested-daemon arrangement yourself.
Use more than one layer when the evaluation has more than one trust boundary
You can run the application under test in a container inside the agent’s Sandbox. Keep the responsibilities separate: the Sandbox contains the autonomous agent, while the inner container models the application runtime. Document whether the inner service is allowed to access the agent, the host proxy, or other guest services.
How do I design a reproducible eval?
Make isolation settings test inputs, not undocumented defaults. A repeatable run should let another engineer reconstruct both the agent’s capabilities and the resources it could touch.
- Define the threat model. State whether generated code is trusted, partially trusted, or fully adversarial; identify the host assets that must remain protected; and define what a successful escape or data leak would look like.
- Choose the boundary. Record container, standalone microVM, or Docker Sandbox; the host operating system and hardware platform; and the relevant Docker and agent versions.
- Select the workspace mode. Record direct, clone, or mountless operation, the initial files supplied, and whether the agent can write back to the host checkout.
- Write the network policy before starting the agent. List allowed TCP destinations, whether any UDP exception is enabled, and the expected behavior for blocked traffic.
- Inventory credentials and integrations. List proxy-injected credentials, mounted files, shared stores, MCP servers, and any host-side process that can answer agent requests.
- Control initial state. Decide whether each run starts from a new Sandbox or reuses one. A local Sandbox retains installed packages and images across stops and restarts until it is removed, so reuse can introduce cache and contamination effects.
- Run capability canaries. Test package installation, nested image build, workspace write, access to an intentionally blocked network destination, access to an allowed service, and attempted discovery of the host Docker socket. Keep the canary results with the main score.
- Measure the workload on the target host. Capture startup time, steady-state latency, CPU, memory, disk, image-transfer time, and failure rate for the actual agent tasks. Run enough repetitions to show variability rather than reporting a single lucky run.
- Export artifacts and reset. Save logs, generated patches, image digests, policy files, and the final agent state. Remove or recreate the Sandbox when the next run must be independent.
What should you measure besides pass or fail?
Docker’s cited materials make a qualitative startup distinction: a Sandbox takes more work to start than a container because a kernel boots. They do not provide controlled numbers for AI-agent eval startup latency, throughput, memory overhead, escape rates, or evaluation outcomes. Measure those values on the platforms and workloads that matter to your team.
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| Measurement | Why it changes the decision | How to record it |
|---|---|---|
| Boundary and kernel sharing | Shows what class of host compromise the setup is intended to resist | Record container, microVM, or Sandbox and any privileged features |
| Docker workflow | Build, run, and Compose requirements may force an in-VM daemon | Record which daemon received each command and where images were stored |
| Workspace exposure | Read-write host mounts can affect both safety and agent productivity | Record mode, mount paths, permissions, and source revision |
| Network behavior | Downloads and external services can dominate results or enable exfiltration | Record allowlists, proxy behavior, blocked protocols, and observed destinations |
| Startup and resource use | VM boot overhead may matter for thousands of short evaluations | Measure cold start, warm start, CPU, memory, disk, and variance on the target host |
| Operational and compute cost | Cloud runs and model calls can outweigh local runtime costs | Separate local infrastructure, cloud Sandbox usage, and model-provider charges |
What are the lifecycle and cost implications?
Local Sandbox state, including installed packages and images, persists through stops and restarts until the Sandbox is removed. That persistence is convenient for interactive development but can invalidate comparisons: a later agent may benefit from a warm package cache or an image built by an earlier run. Use explicit recreation or cleanup when isolation between trials matters.
Docker’s overview states that local Sandbox compute and the CLI are free for commercial work, while cloud compute is pay-as-you-go and model-provider charges are separate. The comparison page labels its pricing estimates as rates current in September 2026. Treat those terms as time-sensitive and confirm the current pricing and cloud limitations before budgeting; do not combine provider charges with Sandbox infrastructure costs in a single unexplained number.
What commonly goes wrong, and how do you fix it?
| Symptom | Likely cause | Correction |
|---|---|---|
| The agent changes files on the developer’s checkout | Direct workspace mode is read-write | Use clone or mountless mode, or make the direct mount an explicit part of the test and protect the checkout with version control |
| A nested build unexpectedly controls host images | The host Docker socket or host daemon endpoint was exposed | Remove that connection and use the Sandbox’s in-VM Engine for the nested workflow |
| Package installation fails even though the agent is online | The package mirror or registry is not in the TCP allowlist | Add only the required destination and rerun the blocked-destination canary |
| A test can reach an internal service that was not intended | Network policy was broader than the written threat model | Reduce the allowlist, inspect proxy rules, and record the corrected policy as a new evaluation condition |
| Runs become faster or produce different builds over time | Persistent packages, images, or caches survived a restart | Recreate the Sandbox for clean trials or label the run as warm-state |
| A host-side tool exposes unexpected data | A local stdio MCP server or other integration runs outside the VM | Remove the integration for untrusted tests or audit it as a trusted host component |
| Results differ widely between laptops | Hypervisor, kernel, CPU, storage, or network conditions differ | Report the host platform and measure each target environment instead of carrying over one machine’s timing |
What is the practical decision rule?
Use this sequence when selecting the default for a new evaluation:
- If the agent only runs a known test command against a fixed application and has no sensitive mounts, start with a container.
- If the agent executes arbitrary generated code or untrusted repository content, move the agent into a microVM-class boundary.
- If that agent must build, run, or compose Docker containers, prefer a Docker Sandbox or another microVM with a private guest daemon rather than exposing the host socket.
- If the evaluation’s purpose is to measure host-kernel behavior, container runtime behavior, or a production deployment that does not use a VM, keep the test in the corresponding environment and state that the boundary is weaker.
- For every option, make workspace, network, credential, integration, and reset policies explicit before comparing scores.
The strongest choice is therefore workload- and threat-model-dependent. A Sandbox is not a faster container and a microVM is not a magic escape-proof box; each is a different boundary whose remaining connections must be configured and tested.
Frequently Asked Questions
Does a microVM prevent an agent from stealing data over the network?
No. A separate guest kernel does not stop an agent from sending data to a destination that the network policy permits. Restrict egress, avoid supplying unnecessary files or credentials, and test the policy with deliberately blocked and allowed destinations.
Can I use a shared Sandbox for multiple unrelated agents?
You can, but persistent packages, images, caches, and files create cross-run state that can leak information or change results. Use separate Sandboxes or recreate the environment when agents are not intended to trust one another.
Should evaluation scores include Sandbox startup time?
Include it when the production scenario launches a fresh environment for each task or when throughput is the constraint. Report cold and warm measurements separately so VM boot overhead is not hidden inside an average task duration.
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What information should accompany a published agent-eval result?
At minimum, report the boundary type, host platform, workspace mode, network allowlist, credential and host integrations, initial and reset state, Docker or agent versions, and whether nested builds used a private guest daemon. Without those details, another team cannot tell whether a score reflects the agent or a more permissive environment.
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