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# Introduction
When you start letting AI agents write and run code, the first critical question is: Where can that code be safely executed?
Running LLM-generated code directly on your application servers is dangerous. It can leak secrets, consume a lot of resources, or even break critical systems, whether by accident or on purpose. This is why agent-native code sandboxes have become increasingly integral parts of modern AI architectures.
With a sandbox, your agent can develop, test, and debug code in a completely isolated environment. Once everything works, the agent can prepare a pull request for you to review and integrate. You get clean, functional code without worrying about untrusted implementations touching your real infrastructure.
In this post, we’ll explore five well-known code sandbox platforms designed specifically for AI agents:
- Modal
- Blacksell
- Daytona
- e2b
- Code sandbox together
# 1. Moodle: Serverless AI compute with agent-friendly sandboxes
Modal A serverless platform for AI and data teams. You define your workloads as code, and Modal runs them on CPU or GPU infrastructure, scaling as needed.
This is an important feature for agents Sandboxes: A secure, virtual environment for running untrusted code. These sandboxes can be launched programmatically, given time to go live, and automatically explode when idle.
What Moodle gives your agents:
- Serverless containers From data pipelines to LLM inference, for Python-first A workloads
- Sandboxed code execution So agents can compile and run code in isolated containers rather than in your central app infrastructure
- Code mentality as everything else Which fits well with agent workflows that generate infrastructure and pipelines dynamically
# 2. Blaxail: Persistent Sandbox Platform
Blacksell is an infrastructure platform that provides production-grade agents with their own compute environments, including code sandboxes, tool servers, and LLMs.
Blacksell’s Sandboxes Designed specifically for agent workloads: save microVMs that spin up quickly, scale to zero when idle, and restart within about 25ms even after weeks.
What Blaxel gives your agents:
- Secure, quick-launching micro VMS To run AI-infiltrated code with full file system and process access
- Scale to zero with fast restartso your long-time agents can “sleep” without burning money, yet still feel state-of-the-state
- SDK and Tools .
# 3. Daytona: Run the AI ​​code
Daytona Started as a cloud-native dev environment, then merged into it Secure the infrastructure to run AI-infused code. It offers stateful, flexible sandboxes designed to be used primarily by AI agents rather than humans.
Daytona has focused on speedy creation of sandboxes: “from code to execution” in its marketing materials is all 90ms, with some sources hovering around 27ms for secure, flexible runtimes.
What Daytona gives your agents:
- Lightning-fast, stateful sandboxes Built for consistent agent workflows
- Safe, isolated runtimeuse Docker by default with support for strong isolation layers like Kinta containers and SysBox
- Full programmatic control File operations, Git, LSP, and more than code execution with a clean, agent-friendly SDK
# 4. E2B: Sandbox for Computer Use Agents
e2b Describes itself Cloud infrastructure for AI agentsoffer isolated sandboxes secured in the cloud that you control via Python and the JavaScript SDK.
A lot of people know E2B from them Code Interpreter Sandbox: A way to give your app a code-driven runtime similar to a “code interpreter”, but under your control and ready for agent workflows.
What E2B gives your agents:
- Open source, sandboxed cloud environment For AI agents and AI-powered apps.
- Code interpreter-style runtime For Python and JS/TS, exposed via SDK and CLI.
- designed for Data analysis, visualization, codegen evals, and full AI generated apps This requires a secure execution layer.
# 5. Code Sandbox Together: MicroVMS for AI coding products
Together together Known for its AI-native cloud: open and proprietary models, inference, and GPU clusters. On top of that they launched Code sandbox togethera microVM-based environment for building AI coding tools at scale.
Together Code Sandbox provides a fast, secure code sandbox for creating full-scale development environments built for AI. This gives teams faster startup times, robust snapshotting, and microVMS configurations with mature dev-environment tooling. Developers use it to power it.
What the Code Sandbox gives your agents:
- Instant VM creation Provisioning new people from snapshot in ~500 ms and from scratch in less than 2.7 seconds (P95)
- Scale from 2 to 64 VCPUs and 1 to 128 GB of RAM, with sizes hot for compute-intensive workloads
- Deep integration together Model library and AI-native cloudso your agents can both generate and execute code on the same platform
# How to choose the right code sandbox for your AI agents
All five options provide a safe, isolated place for agents to run code. Choose based on what you are optimizing for:
- Modal: Python’s first platform for pipelines, batch jobs, training/diagnosis, and sandboxed execution in one place.
- Blakeslee/Daytona: Agent-local sandboxes that rotate quickly and can persist like real workspaces.
- E2B: Code-interpreter style implementation with robust JS + Python SDK and open source routes.
- Sandbox the code together: Perfect fit if you’re developing serious AI coding products and already running on Infra together.
Abid Ali Owan For centuries.@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master’s degree in Technology Management and a Bachelor’s degree in Telecommunication Engineering. His vision is to create an AI product using graph neural networks for students with mental illness.