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AI Agent Code Execution Sandbox

Give code-writing agents a disposable isolated machine: every run executes in its own Firecracker microVM with no shared kernel — E2B, Daytona and Modal class tooling that makes AI-generated code safe to run, roll back and audit

Tool Interface

Interactive tool will be available soon

Features

  • ✓ Hardware-level isolation: Firecracker microVMs separate the guest from the host kernel, far safer than shared-kernel containers
  • ✓ Disposable by design: sandboxes are created on demand and destroyed after use, with snapshots and fast resume so a dirty run never poisons the next one
  • ✓ No egress by default: outbound traffic is sealed unless you allow specific domains and ports, so agents cannot quietly exfiltrate data
  • ✓ Real filesystems and multi-language runtimes: install Python, Node and system packages in one command and keep working artefacts across the session
  • ✓ Secrets stay out of context: credentials are injected sandbox-side at least privilege, never visible in prompts or logs, with every execution auditable

How to Use

  1. List which workflows actually execute AI-generated code, and group them by trust level: read-only compute, file writes, needs egress
  2. Pick the runtime: Firecracker microVM options for the strongest isolation boundary, workspace-style sandboxes for sub-second starts and long sessions
  3. Wire the sandbox into the agent loop: hand generated code to the sandbox and return only stdout and artefacts to the model, never the host machine
  4. Set limits and keep records: cap runtime and concurrency, default to no egress, inject least-privilege credentials and ship execution logs to observability

FAQ

What is an AI agent code execution sandbox?

An isolation environment built specifically to run AI-generated code: instead of executing agent-written scripts on your own servers, you send them to a disposable machine and collect the results. E2B is the most cited example, giving each sandbox its own Firecracker microVM with no shared kernel — the site describes it as every agent gets a machine. See https://e2b.dev/ and https://e2b.dev/docs

Why not just run agent-written code on your own server?

Because models make mistakes and can be manipulated. Generated code may delete files, spin in an infinite loop, pull in sketchy dependencies, or be steered by indirect prompt injection into reading environment variables and shipping secrets out. An isolated sandbox shrinks the blast radius from an entire server to one disposable box. That is why platforms default to sealed egress and sandbox-side credential injection.

MicroVM versus ordinary container — what actually differs?

Whether the guest shares the host kernel. Docker-style containers share one Linux kernel with the host and isolate mainly via namespaces and cgroups, so a kernel privilege-escalation bug can reach the host. Firecracker microVMs give each sandbox its own kernel behind a hardware virtualisation boundary, at a small startup cost. E2B states it plainly on its own site: microVM, firecracker, vsock, no shared kernel. See https://e2b.dev/

How do you choose among the main platforms?

Choose by your hardest constraint. For the strongest isolation boundary, microVM players like E2B; for sub-second starts and unlimited session length, workspace-style sandboxes such as Daytona; for GPU-adjacent inference and training, Modal. Most teams start on a managed sandbox and only revisit self-hosting once usage and compliance needs are clear. See https://e2b.dev/, https://www.daytona.io/docs/ and https://modal.com/docs

How should secrets be handled inside a sandbox?

The rule is that secrets never enter the model context. Credentials should be injected platform-side at sandbox creation, scoped to least privilege and only the endpoints that are needed, never visible in prompts; egress stays sealed by default with per-domain allowlists, and every execution is logged for audit. Even if a prompt is hijacked, the damage is confined to the allowlist and the sandbox lifetime.

How is cost calculated?

Most platforms bill by execution time with CPU and memory priced separately, so the lever is keeping sandboxes alive only while they work: destroy on completion and pause long jobs via snapshots instead of idling. Compare cold-start times too, since they decide how much dead time every short task pays for. Check current pricing at https://e2b.dev/pricing, https://www.daytona.io/pricing and https://modal.com/pricing