Get in Touch

Course Outline

Introduction to OpenAI Codex CLI

  • Understanding what Codex CLI is and its 2025 open-source Rust architecture.
  • Key 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.
  • 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.
  • File read, write, and edit operations from prompts.
  • Shell command execution and piped outputs.
  • Managing working directories and project context.

Approval Modes and Safety

  • Configuring automatic, ask-before-execute, and fully manual modes.
  • Sandboxing and read-only versus write-enabled sessions.
  • Handling destructive commands and file deletions safely.

Git and CI Integration

  • Using Codex CLI to generate commits and diffs.
  • 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.
  • Local inference with Ollama and self-hosted endpoints.
  • Model selection strategies for latency versus quality.

Team Deployment and Governance

  • Shared configuration and secrets management.
  • Usage policies and audit logging for enterprise.
  • 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.
  • Caching and session state management.

Troubleshooting Common Issues

  • Resolving connection failures to backends.
  • Debugging prompt ambiguity and misinterpretations.
  • Handling rate limiting and retry strategies.

Security Best Practices

  • Protecting API keys in shared environments.
  • Preventing prompt injection and command hijacking.
  • Data residency and compliance considerations.

Summary and Next Steps

  • Recap of core capabilities and workflows.
  • Community resources and open-source contributions.
  • Transitioning to advanced multi-agent orchestration topics.

Requirements

  • Experience with software development in any programming language.
  • Basic understanding of command-line and terminal usage.
  • Familiarity with the basics of Git.

Audience

  • Software developers looking to incorporate AI terminal agents into their workflow.
  • DevOps engineers exploring Rust-based AI tooling.
  • Team leads evaluating OpenAI Codex CLI for group adoption.
 14 Hours

Related Categories