From model to agent: Equipping the Responses API with a computer environment

We're currently in a shift from using models, which excel at particular tasks, to using agents capable of handling complex workflows. By prompting models, you can only access trained intelligence. However, giving the model a computer environment can achieve a much wider range of use cases, like running services, requesting data from APIs, or generating more useful artifacts like spreadsheets or reports.

A few practical problems emerge when you try to build agents: where to put intermediate files, how to avoid pasting large tables into a prompt, how to give the workflow network access without creating a security headache, and how to handle timeouts and retries without building a workflow system yourself.

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.

The shell tool

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.

Orchestrating the agent loop

text at the beginning ... 1000 chars truncated ... text at the end