Instead of putting it on developers to build their own execution environments, we built the necessary components to equip the Responses API⁠(opens in a new window) with a computer environment to reliably execute real-world tasks.

OpenAI’s Responses API, together with the shell tool and a hosted container workspace, is designed to address these practical problems. The model proposes steps and commands; the platform runs them in an isolated environment with a filesystem for inputs and outputs, optional structured storage (like SQLite), and restricted network access.

In this post, we’ll break down how we built a computer environment for agents and share some early lessons on how to use it for faster, more repeatable, and safer production workflows.

A good agent workflow starts with a tight execution loop: the model proposes an action like reading files or fetching data with API, the platform runs it, and the result feeds into the next step. We’ll start with the shell tool—the simplest way to see this loop in action—and then cover the container workspace, networking, reusable skills, and context compaction.