A look at the controls, boundaries, and telemetry OpenAI uses to govern coding agents in real workflows.
As AI systems become more capable, they increasingly act on behalf of users. Coding agents can autonomously review repositories, run commands, and interact with development tools. These are tasks that previously required direct human execution.
With Codex, we’ve designed these capabilities alongside the controls organizations need for safe deployment. Security teams need ways to govern how agents operate: what they can access, when human approval is required, which systems they can interact with, and what telemetry exists to explain their behavior.
At OpenAI, we deploy Codex with a few clear goals: keep the agent inside clear technical boundaries, let developers move quickly on low-risk actions, and make higher-risk actions explicit. We also preserve agent-native telemetry so we can understand and audit what the agent did. In practice, that means managed configuration, constrained execution, network policies, and agent-native logs.
We deploy Codex with a simple principle that it should be productive inside a bounded environment, low-risk everyday actions should be frictionless, and higher-risk actions should stop for review.
Approvals and sandboxing work together. The sandbox defines the technical execution boundary, including where Codex can write, whether it can reach the network, and which paths remain protected. Approval policy determines when Codex must ask to perform an action, such as when it needs to do something outside of the sandbox. Users can approve the action once, or approve that type of action for that session.
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