The Sandbox

The most powerful capability of an agent is Code Execution. An agent that can write and run Python can perform math, visualize data, and scrape the web.

But allowing an AI to run essentially exec(model_output) on your server is a critical security risk.

The Risk Vector

If you run AI code on your production backend:

• os.system("rm -rf /") -> Deletes your server.
• os.environ -> Leaks your API keys.
• while True: -> Freezes your CPU (DoS).

The Solution: Sandboxing

You must capture the AI's code and run it in an isolated, ephemeral environment.

1. Docker Containers

Spin up a fresh Docker container for each task.

• Pros: Standard, widely understood.
• Cons: Startup time (~1-2s) is too slow for real-time chat. Security is not perfect (container escapes).

2. Micro-VMs (Firecracker / gVisor)

This is what AWS Lambda and Fly.io use.

• Pros: Strong isolation, fast startup (~100ms).
• Cons: Complex to operationalize yourself.

3. Hosted Code Interpreters (e.g., E2B)

Specialized infrastructure providers like E2B offer instant, secure sandboxes via API.

import { Sandbox } from 'e2b';
 
// 1. Create a secure cloud instance
const sandbox = await Sandbox.create({ template: 'base' });
 
// 2. Run the AI's code
const code = `print("Hello from the sandbox!")`;
const execution = await sandbox.runCode(code);
 
// 3. Get result
console.log(execution.logs.stdout); // "Hello from the sandbox!"
 
// 4. Cleanup
await sandbox.close();

Features of a Good Sandbox

• Isolation: No network access to your internal VPC.
• Limits: Restricted CPU, RAM, and Execution Time.
• State: Ability to keep variables alive between code blocks (like a Jupyter Notebook).
• Filesystem: Ability to upload/download files (for processing CSVs or images).

Summary

Never eval() AI code locally. Use a Sandbox pattern (ideally a specialized provider for speed/security) to give your agent superpowers without giving it the keys to your kingdom.