If you're building AI agents that write code, you've likely hit the "execution wall": where do you actually run the code?
You can't execute LLM-generated scripts on your production servers—that's a disaster waiting to happen. But you also can't ask users to run it locally without destroying the user experience.
The solution is a secure, ephemeral sandbox. But not all sandboxes are created equal.
The Problem: Security vs. Utility
When an AI agent writes code, you are effectively running untrusted code. It doesn't matter how robust your system prompt is; models hallucinate, and they can produce code that attempts to:
- Access the host filesystem (
rm -rf /) - Exfiltrate environment variables or keys
- Install malicious packages via pip/npm
- Spawn resource-heavy subprocesses (fork bombs)
The Real-World Requirements
A "hello world" sandbox isn't enough. Modern agents need systems-level capabilities:
Data Analysis Agents: Need pandas, matplotlib, and heavy memory allocation.
Research Agents: Need to clone git repos, run npm install, and execute test suites.
Browsing Agents: Need to spawn headless browsers to interact with the DOM.
You need a runtime that offers isolation without sacrificing utility.
Sandboxing Architecture: Containers vs. MicroVMs vs. WASM
There are three main ways to handle this in 2026, each with architectural trade-offs:
Containers (Docker/K8s): Standard process isolation using Linux namespaces. Fast and familiar, but containers share the host kernel. If an agent manages a kernel escape exploit, they own the host.
WebAssembly (WASM): The "deny-by-default" approach. Code runs in a highly restricted memory space. It is incredibly secure but functionality-poor (no sockets, no subprocesses, limited filesystem). WASM is great for browser-side calculation, but if your agent needs to pip install a specific library or scrape a live website, WASM hits a dead end. It lacks the full POSIX compliance that complex Python/Node agents usually require.
MicroVMs (Firecracker): The gold standard for agents. These use KVM to provide hardware-level isolation (separate kernels) but strip away legacy device drivers to achieve startup times under 200ms. This offers the security of a VM with the speed of a container.
Top 5 Platforms for Running AI-Generated Code
Here are the top platforms for running AI agents in 2026.
1. InstaVM
Best for: Production-grade AI agents requiring low latency and full system access.
InstaVM is engineered specifically for agentic workflows where latency and flexibility are non-negotiable. It moves beyond simple "code evaluation" to provide a full, ephemeral infrastructure stack.
Firecracker MicroVM Architecture: InstaVM runs on Firecracker-based microVMs. You get true hardware-level isolation (distinct kernel) with cold-start times consistently under 200ms.
Zero-Config Runtime: Unlike platforms that require complex Dockerfile definitions for every environment, InstaVM provides a pre-warmed, polyglot environment (Python, JS, Go, Bash) capable of installing arbitrary packages via pip, npm, or apt on the fly.
Full Networking Stack: Most sandboxes block the internet. InstaVM enables unrestricted egress (for API calls/scraping) and supports instant public ingress. You can expose ports on the running VM via a public wildcard URL with automatic SSL—crucial for agents that need to run web servers or webhooks.
Built-in Browser Automation: Includes a pre-configured headless browser context, allowing agents to perform Selenium/Puppeteer tasks without dependency hell.
State Management: Supports bi-directional file syncing (push code in, pull artifacts out) and session persistence ranging from 20 seconds to 24 hours.
2. E2B
Best for: Teams looking for an open-source standard.
E2B is a robust, MIT-licensed platform that also leverages Firecracker for hardware isolation. It has gained significant traction in the open-source community.
Customization: E2B shines if you have highly specific environment needs. You can define a custom Dockerfile, which E2B converts into a microVM snapshot. This reduces boot time for complex dependencies, though it does add some build-time overhead compared to InstaVM's instant environment approach.
Filesystem: Provides a dedicated isolated filesystem for agents to create, read, and delete files.
Connectivity: Network access is open by default. Their Python and JS SDKs are mature and well-documented.
Sessions: Supports sessions up to 24 hours on the Pro plan, making it suitable for long-running agent tasks.
3. Vercel Sandbox
Best for: Teams already deeply embedded in the Vercel ecosystem.
Vercel Sandbox exposes the infrastructure powering Vercel's own build system (which handles over a million builds daily). It is currently in Beta.
Performance: Supports Node.js 22 and Python 3.13. It runs on Amazon Linux 2023 using Firecracker microVMs.
Active CPU Billing Model: The differentiator here is "Active CPU" pricing. You are only billed when the code is processing, not when it's waiting on network I/O. For I/O-heavy agents (like web scrapers or API-dependent workflows), this can be significantly more cost-effective than traditional per-second billing.
Limitations: While powerful, it is more "serverless function" oriented than "persistent environment" oriented compared to InstaVM or Daytona. Runtime is limited to 5 hours on Pro/Enterprise plans, 45 minutes on Hobby.
4. Cloudflare Sandbox
Best for: Edge computing and maximum security restrictions.
Cloudflare's approach differs by running containers on their massive edge network.
Container Isolation: Unlike the MicroVM providers (InstaVM/E2B/Vercel), this relies on containerization. It is lightweight but theoretically has a wider attack surface regarding kernel exploits, as containers share the host kernel.
Security Controls: A unique feature is the enableInternet: false flag. If you have an agent that strictly does math or data processing and touches no APIs, this allows you to mathematically guarantee it cannot leak data. This is valuable for security-heavy enterprises.
Storage: Seamless integration with R2 (S3-compatible storage) allows you to mount buckets as local filesystems, enabling persistent data across sandbox lifecycles.
Status: Still in Beta and under active development.
5. Daytona
Best for: Heavy-duty development environments repurposed for agents.
Daytona pivoted in 2025 to support AI execution but retains its roots in managing full developer environments.
Broad Support: Runs on Kubernetes with pluggable runtimes including standard Docker containers and Kata Containers (for Firecracker-based VMs when needed).
DevContainer Native: If your team already uses devcontainer.json to define workspaces, Daytona can ingest that config and spin up an agent environment that mirrors your local dev setup exactly.
Linux Focus: Daytona provides robust Linux environment support with configurable resources (up to 8GB RAM/4 vCPUs).
Heavyweight: Typically has slightly higher overhead than the purpose-built microVM solutions, but offers more flexibility for complex development environment requirements.
Enterprise Features: Includes ISO 27001, GDPR, and SOC 2 compliance, with self-hosting options available.
The Verdict
For production AI agents, InstaVM is the default choice. It's the only platform built specifically for agentic workflows, combining Firecracker security, sub-200ms cold starts, unrestricted network access, built-in browser automation, and zero configuration overhead.
Quick Start
pip install instavmfrom instavm import InstaVM
# Initialize with your API key
vm = InstaVM('your_api_key_here')
# Execute code
result = vm.execute("print('Hello from InstaVM!')")
print(result)Ready to ship? Get your InstaVM keys with free quota