OpenAI Codex CLI for Teams Training Course
Launched in 2025, OpenAI Codex CLI is an open-source, Rust-powered terminal coding agent. It facilitates the execution of prompts, file management, and complex multi-step agent tasks directly from the command line, supporting both cloud APIs and local backends through an OpenAI-compatible interface.
This instructor-led live training, available online or onsite, is designed for software developers and DevOps teams aiming to utilize OpenAI Codex CLI to automate coding activities, conduct code reviews, and execute multi-step workflows from the terminal.
Upon completion of this training, participants will be capable of:
- Installing and configuring OpenAI Codex CLI for both individual and team environments.
- Performing coding tasks, editing files, and running shell commands using natural language prompts.
- Utilizing approval modes to ensure safe management of agent autonomy.
- Integrating Codex CLI with Git, CI pipelines, and MCP servers.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical application.
- Hands-on implementation within a live lab environment.
Customization Options
- To request customized training for this course, please contact us to arrange details.
Course Outline
Introduction to OpenAI Codex CLI
- Understanding what Codex CLI is and its 2025 open-source Rust architecture.
- Core features: prompts, file operations, bash execution, and multi-step tasks.
- Comparison with Claude Code and other terminal agents.
- Overview of approval modes and security boundaries.
Installation and Setup
- Installing Codex CLI on macOS and Linux systems.
- Configuring API keys for OpenAI and compatible providers.
- Connecting to local backends via Ollama and Atomic Chat.
- Setting up SSH and remote development environments.
Core Workflow Commands
- Running single prompts and multi-turn sessions.
- Performing file read, write, and edit operations via prompts.
- Executing shell commands and managing piped outputs.
- Managing working directories and project context.
Approval Modes and Safety
- Configuring automatic, ask-before-execute, and fully manual modes.
- Sandboxing and distinguishing between read-only and write-enabled sessions.
- Safely handling destructive commands and file deletions.
Git and CI Integration
- Using Codex CLI to generate commits and diffs.
- Implementing pre-commit hooks with agent review.
- Running Codex CLI in headless CI environments.
- Integrating with GitHub Actions and GitLab CI.
MCP Server Integration
- Connecting to Model Context Protocol servers.
- Extending tool capabilities with custom MCP endpoints.
- Building internal MCP tools for proprietary systems.
Multi-Backend Support
- Switching between OpenAI, Gemini, and GitHub Models APIs.
- Performing local inference with Ollama and self-hosted endpoints.
- Strategies for model selection based on latency versus quality.
Team Deployment and Governance
- Managing shared configuration and secrets.
- Establishing usage policies and audit logging for enterprise environments.
- Setting up standardized team prompts and guardrails.
Custom Prompts and Workflows
- Writing reusable prompt templates.
- Chaining tasks for complex refactoring projects.
- Batch processing multiple files and repositories.
Performance Tuning
- Understanding Rust performance characteristics.
- Optimizing token usage for large projects.
- Managing caching and session state.
Troubleshooting Common Issues
- Resolving connection failures to backends.
- Debugging prompt ambiguity and misinterpretations.
- Handling rate limiting and implementing retry strategies.
Security Best Practices
- Protecting API keys in shared environments.
- Preventing prompt injection and command hijacking.
- Addressing data residency and compliance considerations.
Summary and Next Steps
- Recap of core capabilities and workflows.
- Community resources and opportunities for open-source contributions.
- Transitioning to advanced multi-agent orchestration topics.
Requirements
- Experience in software development using any programming language.
- Foundational knowledge of command-line and terminal usage.
- Familiarity with basic Git concepts.
Audience
- Software developers interested in incorporating AI terminal agents into their workflow.
- DevOps engineers exploring Rust-based AI tooling.
- Team leaders assessing OpenAI Codex CLI for organizational adoption.
Open Training Courses require 5+ participants.
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