OpenAI Codex CLI for Teams Training Course
Launched in 2025, OpenAI Codex CLI is an open-source, Rust-based terminal coding agent. It allows users to execute prompts, perform file operations, and manage 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 leverage OpenAI Codex CLI to automate coding workflows, review code, and execute complex multi-step processes via the terminal.
Upon completion of this training, participants will be equipped to:
- Install and configure OpenAI Codex CLI for both individual and team environments.
- Perform coding tasks, edit files, and run shell commands using natural language prompts.
- Utilize approval modes to ensure safe management of agent autonomy.
- Seamlessly integrate Codex CLI with Git, CI pipelines, and MCP servers.
Course Format
- Interactive lectures paired with meaningful discussions.
- Extensive exercises and practical practice sessions.
- Hands-on implementation within a live-lab environment.
Course Customization Options
- To request customized training for this course, please contact us to arrange your requirements.
Course Outline
Introduction to OpenAI Codex CLI
- Understanding Codex CLI and its 2025 open-source Rust architecture
- Key features: prompt handling, file operations, bash execution, and multi-step task management
- Comparative analysis 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 service providers
- Connecting to local backends via Ollama and Atomic Chat
- Setting up SSH and remote development environments
Core Workflow Commands
- Executing single prompts and managing multi-turn sessions
- Performing file read, write, and edit operations through prompts
- Running shell commands and handling piped outputs
- Managing working directories and project context
Approval Modes and Safety
- Configuring automatic, ask-before-execute, and fully manual modes
- Implementing 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-based reviews
- 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 via custom MCP endpoints
- Developing 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
- Adopting model selection strategies to balance latency versus quality
Team Deployment and Governance
- Managing shared configurations and secrets
- Establishing usage policies and audit logging for enterprise compliance
- Setting up standardized team prompts and security guardrails
Custom Prompts and Workflows
- Creating 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-scale projects
- Implementing caching and session state management strategies
Troubleshooting Common Issues
- Resolving connection failures to backend services
- Debugging prompt ambiguity and misinterpretations
- Handling rate limiting and implementing retry strategies
Security Best Practices
- Protecting API keys in shared development environments
- Preventing prompt injection and command hijacking
- Addressing data residency and compliance requirements
Summary and Next Steps
- Recap of core capabilities and established workflows
- Exploring community resources and open-source contribution opportunities
- Bridging to advanced multi-agent orchestration topics
Requirements
- Experience in software development using any programming language
- Familiarity with basic command-line and terminal operations
- Understanding of fundamental Git concepts
Audience
- Software developers seeking to incorporate AI terminal agents into their workflow
- DevOps engineers interested in exploring Rust-based AI tooling
- Team leads assessing OpenAI Codex CLI for enterprise-wide adoption
Open Training Courses require 5+ participants.
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