To run AI-generated code more safely, give it a separate execution environment rather than treating each tool call as ordinary application code. A microVM can provide a VM-level boundary with its own operating system, filesystem, network rules and lifecycle. AWS Lambda MicroVMs is one managed option: initialize an application, capture it as a snapshot, launch a separate instance for a session or job, then suspend or terminate it as needed. That boundary helps contain code, but it does not replace decisions about credentials, network access, mounted files or which actions the agent is allowed to take.
What changes when code runs in a microVM?
An ordinary stateless function invocation is designed to run a bounded piece of work within a serverless function model. A microVM-based sandbox instead gives a session or job a virtual machine environment that can run an operating system and retain state across its own lifecycle. That makes it a better fit when an agent needs shell commands, OS packages, existing developer tools or a workspace that persists while the session is active.
A useful mental model is: prepare an environment, snapshot it, launch a distinct microVM for a session or job, decide what can cross its boundary, and suspend or terminate it according to the workload. Each instance starts from the same prepared image, but its runtime state can be separate. AWS describes Lambda MicroVMs as a managed serverless environment with VM-level isolation and full OS capabilities, including for user- or AI-generated code.
This is not a claim that every microVM service is equivalent or that a VM boundary makes arbitrary code harmless. The controller still determines what the sandbox can access, and the service’s actual isolation and lifecycle depend on its implementation.
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How AWS Lambda MicroVMs fit the pattern
Build and capture a starting environment
AWS’s documented workflow packages application code and a Dockerfile into an archive and uploads it to S3. Lambda provisions a fresh microVM, builds the environment by executing the Dockerfile, starts the application, optionally waits for a readiness response, and captures the resulting memory and disk state as a snapshot.
Capturing initialized state can avoid repeating dependency installation and application startup for every session. The snapshot is also a shared starting point: data created while building it may appear in every instance launched from it. AWS advises creating unique values after launch through the runtime hook. Generate session-specific secrets, IDs and other unique content at runtime, not during image creation.
Run, suspend, resume or terminate
- Launch: A caller invokes
run-microvm. The application is restored from its snapshot and exposed through a dedicated HTTPS endpoint. - Execute: The agent’s orchestrator sends work to the session’s execution environment. Keep orchestration and policy enforcement outside the untrusted-code environment where the architecture requires them to remain trusted.
- Suspend: When idle, suspend the instance if retaining its memory and disk state is useful. AWS documents resumption on traffic or through an explicit API call.
- Resume: Continue the session from its retained state rather than rebuilding the environment from scratch.
- Terminate: End the instance when its work is complete and release its resources.
AWS documentation describes a maximum session lifetime of up to eight hours. Treat that as a vendor-stated service limit, not a general property of microVMs, and check current service documentation before designing around it.
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Keep orchestration separate from execution
In AWS’s agent-sandbox example, the agent orchestrator and session handling sit outside the execution VM; the Lambda MicroVM serves as the tool-call worker environment. The intended separation gives each session its own filesystem, credentials and network boundary so concurrent users do not inadvertently share execution state. AWS’s September 18, 2026 description presents per-environment Firecracker isolation, snapshot-based launch and vertical scaling as service properties; those are AWS-described capabilities, not independent comparative test results.
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What the microVM boundary does—and does not—protect
A VM boundary is a useful layer of isolation, but explicit connections to the host or other systems remain part of the trust boundary. For example, Docker’s sandbox security documentation describes a sandbox agent with broad privileges inside the microVM and identifies workspace mounts, outbound connections, forwarded credentials, skills and MCP traffic as configured connections across that boundary.
Workspace access
- Direct workspace mount: Docker documents this as read-write; changes made in the sandbox are visible on the host.
- Clone mode: The repository is mounted read-only and the sandbox receives a private clone for its work.
- No workspace mount: A mountless sandbox has no host workspace mounted into it.
Choose based on whether the job needs to modify the host’s working copy or can work on a private copy. A VM does not make a mounted host directory private.
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Network access
Docker documents network requests passing through a host proxy and policy. In that product’s documented setup, outbound TCP is governed by network policy; UDP is blocked by default unless an experimental feature is enabled, and ICMP is blocked. Defaults can include broad wildcard domains, so inspect the active rules rather than assuming the sandbox has narrow egress. AWS separately describes configurable ingress and egress for Lambda MicroVMs; do not infer that Docker’s specific network behavior applies to AWS.
Credentials and host integrations
Docker documents a design in which a host-side proxy can inject credentials into outbound HTTP request headers without placing raw credential values in the VM. This is a product-specific mechanism, not a general property of microVMs. Local stdio MCP servers run on the host, outside the sandbox VM, so treat them as trusted host integrations rather than code contained by the microVM.
Agent permissions
Isolation answers where code executes; it does not decide which tools an agent may call or whether it may deploy, publish or modify production systems. AWS’s secure-code-execution guidance treats execution isolation, current domain expertise and deterministic governance as separate layers. Apply tool-level permissions and approval rules in the trusted control plane as well as restricting the execution environment.
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When a microVM sandbox is a good fit
AWS lists interactive code environments, AI code execution, analytics using supplied scripts, security scanning, reinforcement-learning environments, multi-tenant CI/CD and game servers running user scripts as candidate uses. The common need is to run untrusted or user-supplied code with OS-level capabilities and to control a distinct environment’s lifecycle.
- Use a microVM approach when the workload needs an OS, packages or existing tools that are awkward to provide in a narrowly scoped function.
- Prefer a session- or job-level environment when users or concurrent agent sessions must not share execution state.
- Consider an ordinary function when the task is short, bounded and does not need a persistent workspace or broader OS environment.
- Do not choose solely on the word “isolated.” Review what is mounted, proxied, exposed and retained in the specific implementation.
How to compare a microVM sandbox with other execution options
There is no universal winner between functions, containers, microVMs and other sandbox technologies. Compare the behavior your application needs, then test representative workloads under the same conditions. The available AWS and Docker documentation describes product designs; it does not establish a controlled performance or security comparison among microVMs, containers, gVisor and other services.
| Decision factor | What to evaluate |
|---|---|
| Isolation boundary | Whether the implementation provides a VM boundary, what kernel or host components remain shared, and what resources are explicitly exposed. |
| Compatibility | Whether the code needs OS packages, shell access, background processes or existing tools that the chosen environment supports. |
| Launch and resume | Measure cold launch, snapshot restoration and resume behavior for your own application. The sources cited here do not provide an independent comparative benchmark. |
| Filesystem and network policy | Check mount mode, write visibility, allowed destinations, proxy behavior and whether credentials or host integrations cross the boundary. |
| State and cleanup | Decide what should persist across idle periods, how long sessions may live, and when to terminate environments and remove their data. |
| Operations and cost | Account for image building, policy management, observability, idle time, active execution and cleanup under your actual workload. Current service pricing is not established here. |
For an apples-to-apples evaluation, run realistic jobs and record the environment configuration, launch and resume method, workload duration, idle pattern, resource allocation and measurement conditions. Avoid treating a vendor’s maximum lifetime, advertised capability or unrelated invocation-scale figure as a performance result.
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AWS availability and figures to verify
AWS’s June 22, 2026 announcement listed Lambda MicroVMs in US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Tokyo) and Europe (Ireland). That was the announced list on that date, not a guarantee of current availability; confirm the Regions supported for your account and intended deployment before choosing a location.
AWS’s September 18, 2026 Compute Blog described initial allocations from 0.25 vCPU and 0.5 GB to 4 vCPU and 8 GB, with an instance able to scale up to four times its initial CPU and memory allocation without recreation. AWS’s launch blog also described a default baseline of 1 vCPU and 2 GB of memory, with a maximum baseline of 4 vCPUs and 8 GB. These are AWS product statements, not benchmarks or defaults that should be assumed current without checking the service documentation.
AWS’s developer guide uses “15 trillion+ monthly invocations” to describe the scale of Lambda Functions powered by Firecracker. That is an AWS figure about Lambda Functions, not a measure of Lambda MicroVM performance, security or adoption.
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