Why You Should Run AI-Generated Code in a Sandbox

At their best, code generation LLMs reduce cognitive load, accelerate iteration, and serve as a great pair programmer for well-scoped tasks. That said, they also introduce a level of risk. Whether it’s using a variable that was never declared, making up functions that aren’t part of a class, using code from outdated packages, or misdiagnosing an issue, code generation models can create problems.

The issue with code generation is actually much bigger than losing a bit of time from a bad code suggestion. Without proper guardrails, an LLM has the potential to make changes anywhere within the system its running and to any of the resources it’s connected to.

This is why sandboxed execution has become necessary for AI agents and workflows. By isolating code generation and execution inside a sandbox, you allow agents to explore, test, and iterate freely while limiting their impact to a controlled environment. The result is high-velocity experimentation without exposing production code, credentials, or infrastructure.

Ideal Use Cases for Sandboxes

Coding Agents

One of the most effective uses of a sandbox is running coding agents that are allowed to write, modify, and execute code end-to-end. Coding agents stop being useful the moment you restrict them to read-only suggestions. The most useful and exciting results come when an agent can write files, refactor code, install dependencies, run builds, observe failures, and then iterate.

That level of autonomy is also where things go wrong. Agents routinely:

• Modify files outside the intended scope
• Introduce incompatible or insecure dependencies
• Leave the workspace in a partially broken state after a failed run

A sandbox turns these failure modes into acceptable outcomes. The agent is free to free to make changes because those changes are isolated, ephemeral, and disposable. If the environment breaks, you tear it down and start over without ever risking your local environment.

Model and Prompt Evaluation

Evaluating LLMs on code generation requires more than comparing text output. Sandboxes are also well-suited for testing and comparing LLM behavior under identical conditions.

You can issue the same prompt to multiple models, each running in its own sandbox, and evaluate the resulting artifacts side by side. This is particularly useful for code generation, where output quality is best judged by execution rather than text alone.

Even with a single model, running multiple parallel sandboxes against the same prompt often produces meaningfully different results. In many cases, the fastest path to a high-quality result isn’t more prompting, it’s parallel execution and selection.

Tenant-Level Isolation

Multi-tenant agent systems fail in predictable ways when isolation is insufficient. Shared file systems, shared caches, or shared execution contexts lead to cross-tenant contamination. In multi-tenant systems, sandboxes provide a clean boundary for both security and correctness.

By scoping agents to tenant-specific environments, you prevent accidental data leakage, cross-tenant state mutation, and shared dependency conflicts. Each agent operates with access only to the resources explicitly provisioned for that tenant, while the underlying system remains stable and predictable.

More tenants simply mean more sandboxes, not more complexity in your core infrastructure.

Getting Started with Koyeb Sandboxes

Chances are, if you’ve used any code-gen tools online that include a preview of the generated code running, you’ve already interacted with a sandbox even if you didn’t configure it yourself.

Koyeb Sandboxes provide a fully isolated environment for running AI-generated code, without needing to worry or manage the underlying infrastructure.

If you want to get started with Koyeb Sandboxes, here are some resources to help you:

• Start with the Koyeb Sandbox Quickstart to get up and running in minutes.
• Review the Sandbox lifecycle to understand how sandboxes are created, run, and cleaned up.
• Learn how to take actions on files and folders within your sandboxes.
• See how to run commands inside your sandboxes for executing code or automating tasks.
• Check out our tutorials on secure code execution using OpenAI Codex and the Claude Agent SDK with Koyeb Sandboxes and the Python SDK.

At Koyeb, we provide high-performance, serverless infrastructure for running AI-generated and untrusted code safely at scale. Our platform runs fully isolated, secure microVMs on bare-metal servers around the world with autoscaling, scale-to-zero, and minimal cold starts.

Interested in the work we’re doing? We’re hiring!