In the most recent episode of Zero to Agent in 30 Minutes, AI engineer Sajal Sharma showed how to use a remote sandbox to let your agents install software, run commands, and control a browser without endangering your everyday machine. In effect, you’re giving your agents a computer of their own.
Sajal demonstrated both approaches with E2B. In one example, an agent downloaded a dataset, installed the packages it needed, analyzed the data, and produced a report inside the sandbox. In another, an agent opened a browser on a remote desktop and searched IKEA for furniture. The demos put both command-line and GUI-based computer use to work on a separate machine.
If you want to follow along or try the same setup, Sajal shared the demo code in his GitHub repo.
How to give an agent its own computer, step-by-step
- Choose how the agent will use the computer. Sajal demoed two ways to work with a remote machine. In the first, the agent used shell commands and files to install packages and process data. In the second, it worked through the graphical interface by reading screenshots and sending mouse and keyboard actions.
- Create an isolated sandbox. For the first demo, Sajal created an E2B sandbox before starting the agent loop. He configured LangChain Deep Agents to send command execution to that remote environment. The agent still did its reasoning locally, but package checks, installs, and data processing ran inside the sandbox.
- Transfer the files you need. Files on your local machine aren’t available in a remote sandbox unless you move them there. Sajal’s agent downloaded the dataset it needed inside the sandbox, created its report there, and then transferred the finished report back to the local machine.
- Map the agent’s actions to the remote desktop. In the GUI demo, Sajal used the OpenAI Agents SDK and built an E2B computer class that connected model actions to the desktop. He mapped screenshots, clicks, keystrokes, and scrolling to the corresponding E2B operations. An early version of the demo crashed because one of those actions wasn’t mapped, so he had to add the missing behavior before the demo could run without crashing.
- Tell the agent what environment it has. Sajal gave the agent basic operating instructions, including which browser was installed. That kept it from spending tokens figuring out how to use the machine. He also recommended giving agents their own task-specific credentials or secrets instead of reusing a person’s authentication profile.
A separate computer also helps when multiple agents need to work at the same time. Sajal used frontend development as an example. Two agents making UI changes might otherwise try to start development servers on the same port or inspect the wrong running instance. With a sandbox for each agent, they can start their own servers and test their own changes. Each run can also start on a fresh machine that gets deleted when the task is done.
Coming next week
Next week, author and AI innovator Bruce Hopkins will show how to build your first agent with the Model Context Protocol (MCP). He’ll demonstrate how to take existing HTTP REST APIs and make them available through MCP so an agent can use those services as tools.
Follow along with Zero to Agent in 30 Minutes on Radar, or watch the latest episode on YouTube, Spotify, Apple, or wherever you get your podcasts. If you’re an O’Reilly member, you can watch live. Save your seat.
