OpenAI Codex Full Course: AI Coding Agent Tutorial with Python Examples

In this first episode of the Codex AI Agent course, we explain the OpenAI Codex architecture and show how Codex works as a true AI coding agent for developers, not just an autocomplete tool. You’ll learn how Codex uses the CLI, IDEs, web interface, GitHub Actions, CI/CD workflows, LLMs, guidance layers, tools, and function calling to power scalable agentic coding workflows for Python projects, AI software engineering, debugging, refactoring, automation, and modern AI-powered development.

In this 2nd episode of the OpenAI Codex Tutorial, we build a real Python backend with Codex as a true AI coding agent in a live coding workflow. You’ll create a FastAPI spinning wheel randomizer API, design a POST /api/pick-winner endpoint, generate and refine backend code with Codex, write automated tests with pytest, set up the project with uv, and take the code from idea to GitHub — perfect for developers learning OpenAI Codex, agentic coding workflows, FastAPI projects, REST API design, AI-powered coding, and modern Python backend development.

In this 3rd episode of the Codex AI Agent course, we continue building our randomizer wheel web application and use Agentic Planning Mode in OpenAI Codex to guide AI code generation for the frontend. You’ll learn how to review, refine, and supervise the Codex implementation plan, align AI-generated code with the system design from the previous episode, and steer agentic coding workflows toward maintainable, modular software architecture for real-world Python and full-stack web development.

In this episode of the Codex AI Agent course, we explore how to scale development with the OpenAI Codex AI coding agent using structured operational policies, AGENTS.md, AGENTS.override.md, GitHub Actions, CI/CD pipelines, branch coverage enforcement, reusable workflows, and automated PR coverage reporting for real-world Python projects. This OpenAI Codex tutorial demonstrates practical OpenAI Codex Python examples, showing how to use codex /init, manage large repositories, and implement scalable agentic coding with Python for production-ready backend systems. Whether you are looking for a Codex tutorial, Codex full course, AI coding assistant tutorial, AI coding agent tutorial, Python AI coding tutorial, or want to learn Codex for beginners, this lecture covers modern AI-powered software engineering workflows, automated testing, CI/CD automation, operational policies for AI code agents, and best practices for scaling AI-assisted development in growing repositories and production environments.

In this Codex AI Agent course episode, we create a fastapi-dockerizer skill with Skill Creator to teach OpenAI Codex repeatable FastAPI Docker workflows using uv, .dockerignore, justfile, and /health smoke checks, while covering explicit $ invocation, implicit skill triggering, progressive disclosure, and best practices for focused agentic coding with Python.

AI models are powerful, but they often need access to external knowledge and services to produce reliable results. In this episode, we explore the Model Context Protocol (MCP) and learn how it extends Codex by connecting it to external tools and documentation sources. We use Context7 MCP to provide always up-to-date Python and library documentation during code generation, making AI-assisted development more accurate and practical. We then implement a production-style continuous deployment pipeline by provisioning infrastructure with Terraform, deploying a containerized application to Google Cloud Run, and automating infrastructure updates through GitHub Actions. By the end of the episode, you’ll understand how MCP tools enhance AI workflows and how to build an automated deployment process that keeps your application and infrastructure in sync.

It would be great to have an experienced reviewer available for every pull request. In this episode, we learn how to integrate Codex directly into a GitHub repository so it can automatically review code changes as part of your continuous integration workflow. You’ll discover how to automate AI-powered feedback on pull requests and releases, incorporate Codex-driven quality checks into your CI pipeline, and use GitHub Actions to make AI an active participant in your development process. By the end of the episode, you’ll know how to add automated AI reviews to your repository and improve code quality before changes are merged.