Build a secure computer for your agent

Name: Build a secure computer for your agent
Uploaded: 2026-07-23T22:44:09.000Z
Duration: 36 min 51 s
Channel: LangChain
Description: LangChain presents architecture patterns for secure, isolated execution environments for AI agents that need to run code. The session covers production-ready sandboxing, credential management, resource controls, and when to build versus using managed solutions.

LangChain23 July 2026Watch on YouTube

Part of series

Ep. 12 · LangChain Deep Agents

Stap-voor-stap uitleg en tutorials over het bouwen, configureren en deployen van Deep Agents met het LangChain-framework.

View the series

What you'll learn

AI agents executing code need more than tools: a complete workspace with file system, shell, installed packages, and persistent state across steps.
Production-ready sandboxing requires secure execution, credential management, resource limits, fast provisioning, snapshots, forks, and persistent state.
Choosing between building your own sandbox versus using a managed solution depends on isolation, security, scalability, and cost requirements.

Frequently asked questions

Why is sandboxing important for AI agents running code?

Model-generated code is untrusted, and direct execution without isolation introduces security risks, scalability challenges, and loss of control over what agents actually execute.

What are the key differences between building your own sandbox and using a managed solution?

Self-built sandboxes offer more control but require more engineering, while managed solutions are faster to implement but may have limitations in isolation, credential safety, and cost controls.

What components are essential for production-ready sandboxed execution?

Secure execution, credential management, resource limits, fast provisioning, snapshots, forks, and persistent state are all needed to run agents safely and reproducibly in production.

How do coding agents, data analysis agents, and file-processing workflows differ in isolated environments?

These different agent types have different workspace requirements and behaviors in sandboxes, and choosing the right infrastructure unlocks new capabilities for each.

Topics

ai agents sandboxing secure execution infrastructure

Description from the channel

Most agents today aren't just calling APIs. They're writing and running code, processing files, browsing the web, iterating across multi-step tasks. That means they need more than tools, they need a real workspace: a file system, a shell, installed packages, and state that persists across steps. But the moment you give an agent execution access, you've introduced a new category of production problem. Model-generated code is untrusted. Local execution doesn't scale. And vanilla containers fall short when you actually need isolation, credential safety, cost controls, and the ability to reproduce what an agent did three sessions ago. In this session, Amy and Mukil walk through how to give agents their own computer without giving up control: - The architecture patterns behind production-ready sandboxed execution and when to build your own vs. use a managed sandbox - What "production-ready" actually requires: secure execution, credential management, resource limits, fast provisioning, snapshots, forks, and persistent state - How coding agents, data analysis agents, and file-processing workflows behave differently in isolated environments, and what becomes possible when infrastructure stops being the bottleneck