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Course Outline
Introduction to OpenAI Codex CLI
- Understanding what Codex CLI is and its 2025 open-source Rust architecture.
- Key features: prompt handling, 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 using 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 via 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.
- Sandboxing techniques: read-only versus 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 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 through 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.
- Strategies for selecting models based on latency versus quality.
Team Deployment and Governance
- Managing shared configurations and secrets.
- Establishing usage policies and audit logging for enterprise use.
- Setting up standardized team prompts and guardrails.
Custom Prompts and Workflows
- Creating reusable prompt templates.
- Chaining tasks for complex refactoring projects.
- Batch processing multiple files and repositories.
Performance Tuning
- Understanding the performance characteristics of Rust.
- Optimizing token usage for large projects.
- Caching and session state management.
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.
- Considering data residency and compliance requirements.
Summary and Next Steps
- Recap of core capabilities and workflows.
- Access to community resources and open-source contributions.
- Transitioning to advanced topics in multi-agent orchestration.
Requirements
- Experience in software development using any programming language.
- Familiarity with basic command-line and terminal operations.
- Basic understanding of Git.
Target Audience
- Software developers interested in incorporating AI terminal agents into their workflow.
- DevOps engineers exploring Rust-based AI tooling.
- Team leads assessing OpenAI Codex CLI for organizational adoption.
14 Hours
