OpenAI has outlined its strategy for safely deploying the Codex coding agent, emphasizing a balance between developer productivity and enterprise security. As AI systems increasingly act autonomously to review repositories, execute commands, and interact with development tools, organizations require robust controls to govern agent behavior. OpenAI's approach focuses on keeping the agent within clear technical boundaries while allowing low-risk actions to proceed frictionlessly and halting higher-risk activities for human review. The deployment architecture relies on three core principles: managed configuration, constrained execution, and agent-native telemetry. A sandbox environment defines the technical boundaries, specifying where the agent can write, which network destinations it can reach, and which paths remain protected. Complementing this is an approval policy that determines when the agent must request permission, particularly for actions occurring outside the sandbox or involving unfamiliar domains. To streamline workflows, OpenAI introduced an Auto-review mode. This feature utilizes a subagent to automatically approve low-risk, routine requests by analyzing the planned action and context, thereby reducing interruptions while maintaining safeguards against high-risk or unintended consequences. Network security is enforced through a managed policy that permits expected destinations while blocking unknown or dangerous ones. Authentication is similarly tightly controlled; credentials for the Command Line Interface and Model Context Protocol are stored in the secure OS keyring, with login enforced through the ChatGPT enterprise workspace. This integration ensures that all Codex activity is tied to workspace-level controls and visible in the ChatGPT Compliance Logs Platform. Furthermore, the system distinguishes between benign shell commands used in daily engineering and those that are potentially dangerous, allowing the former to pass without approval while blocking or reviewing the latter. These controls are implemented through a combination of cloud-managed requirements, macOS preferences, and local configuration files that cannot be overridden by individual users. Beyond control, visibility is critical for security teams. Traditional logs often explain what happened but fail to clarify why an agent took a specific action. To address this, Codex supports OpenTelemetry log export, capturing details such as user prompts, approval decisions, tool execution results, and network events. For enterprise and education customers, these logs are also accessible via the OpenAI Compliance Platform. OpenAI employs these logs alongside an AI-powered security triage agent. When an endpoint alert flags unusual activity, the triage agent analyzes Codex logs to inspect the original request, tool usage, and approval history. This helps security teams distinguish between expected agent behavior, benign errors, and genuine threats requiring escalation. Operationally, these telemetry streams are also used to monitor internal adoption rates, track tool usage patterns, and identify areas where configurations need tuning. By providing these specific control surfaces and agent-aware insights, OpenAI aims to enable security teams to adopt coding agents with greater confidence, ensuring that the integration of autonomous systems meets enterprise security standards.