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# Entry
When you start allowing AI agents to write and run code, the first critical question is: where can this code be safely executed?
Running LLM-generated code directly on application servers is risky. It may reveal secrets, consume too many resources, or even break crucial systems, either accidentally or intentionally. That’s why sandboxes with native code for agents have quickly become vital elements of current artificial intelligence architecture.
With a sandbox, your agent can create, test, and debug code in a completely isolated environment. Once everything works, the agent can generate a pull request for you to review and connect. You get pristine, functional code without having to worry about untrusted execution touching your real infrastructure.
In this post, we’ll look at five leading code sandbox platforms designed specifically for AI agents:
- Modal
- Blaxel
- Daytona
- E2B
- Together Code Sandbox
# 1. Modal: Serverless AI computing with agent-friendly sandboxes
Modal is a serverless platform for AI and data teams. You define your workloads as code, and Modal runs them on CPU or GPU infrastructure, scaling up and down as needed.
One of its key features for agents is sandboxes: safe and sound, ephemeral environments for running untrusted code. These sandboxes can be started programmatically, after a specified lifetime, and automatically deleted when idle.
What Modal gives your agents:
- Serverless containers for Python-based AI workloads, from data pipelines to LLM inference
- Code execution in sandbox mode so agents can compile and run code in isolated containers rather than on the main application infrastructure
- An “everything as code” mindset. which fits well with agent workflows that dynamically generate infrastructure and pipelines
# 2. Blaxel: Eternal sandbox platform
Blaxel is an infrastructure platform that provides production-grade agents with their own compute environments, including code sandboxes, tool servers, and LLM.
Blaxel Sandboxes are designed specifically for agent-based workloads: secure micro-VMs that start up quickly, scale to zero when idle, and resume in approximately 25 ms even after weeks.
What Blaxel gives your agents:
- Secure, instantly bootable micro-virtual machines to run AI-generated code with full access to the file system and processes
- Scale to zero with quick resumeso that your long-lived agents can “sleep” without spending money and at the same time maintain a state of balance
- SDK and tools (CLI, GitHub integration, Python SDK) for deploying agents and connecting to Blaxel resources such as tool servers and batch jobs
# 3. Daytona: Run AI code
Daytona it started as a cloud-native development environment and then transformed into a development environment secure infrastructure for running AI-generated code. It offers stateful, elastic sandboxes designed to be used primarily by AI agents rather than humans.
Daytona focuses on rapid sandboxing: its marketing materials list code-to-execute times of less than 90 ms, and some sources cite secure, elastic runtimes of around 27 ms.
What Daytona gives your agents:
- Lightning-fast, stateful sandboxes Created with agents’ continuous workflow in mind
- Secure, isolated execution environmentsby default using Docker with support for stronger isolation layers such as Kata Containers and Sysbox
- Full program control over file operations, Git, LSP and code execution via a pristine, agent-friendly SDK
# 4. E2B: Sandbox for desktop agents
E2B describes himself as cloud infrastructure for AI agentsoffering secure isolated cloud sandboxes that you can control with Python and JavaScript SDKs
Many people know E2B from their own Code interpreter sandbox: A way to provide your application with a runtime environment that runs code, similar in spirit to a “Code Interpreter”, but under your control and tuned to your agent workflows.
What E2B gives your agents:
- Open-source and sandboxed cloud environments for AI agents and applications using artificial intelligence.
- A code interpreter-style runtime for Python and JS/TS, provided via SDK and CLI.
- Designed for data analysis, visualization, codegen evaluation and full AI-generated applications that require a secure execution layer.
# 5. Together Code Sandbox: MicroVM for AI coding products
Total AI is known for its AI-based cloud: open and specialized models, inference and GPU clusters. Besides, they took off Together Code Sandboxa microVM-based environment for building large-scale AI coding tools.
Together Code Sandbox provides quick and secure code sandboxes for creating full-scale development environments built specifically for AI. It provides teams with configurable microVMs with quick startup times, reliable snapshots, and mature development environment tools. Developers apply it to power next-generation AI coding tools and agentic workflows on a scalable, high-performance infrastructure.
What Together Code Sandbox gives your agents:
- Instantly create virtual machines from snapshot in ~500 ms and share recent ones from scratch in less than 2.7 seconds (P95)
- Scaling from 2 to 64 virtual processors and 1 to 128 GB of hot-swappable RAM for compute-intensive workloads
- Deep integration with Together’s model library and cloud native for artificial intelligenceso your agents can both generate and execute code on the same platform
# How to choose the right code sandbox for AI agents
All five options provide agents with a safe and sound, isolated place to run code. Choose based on this, for optimization:
- Modal: The first Python platform for pipelines, batch jobs, training/inference, and sandboxed execution in one place.
- Blaxel/Daytona: Agent-native sandboxes that launch quickly and can act like a real workspace.
- E2B: Code interpreter style execution with powerful JS + Python SDKs and open source roots.
- Total code sandbox: Best suited if you are building earnest AI coding products and are already using the Together infrastructure.
Abid Ali Awan (@1abidaliawan) is a certified data science professional who loves building machine learning models. Currently, he focuses on creating content and writing technical blogs about machine learning and data science technologies. Abid holds a Master’s degree in Technology Management and a Bachelor’s degree in Telecommunications Engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.