Codex · Part 1

Codex — Part 1 — What It Is and Why It Matters for IT Engineers

Codex is OpenAI’s terminal-based coding agent, built to read, edit, and run commands inside a real project rather than answer questions in a chat window, and that difference matters just as much for infrastructure work as it does for application code.

Quick idea: Codex is an agentic coding tool from OpenAI that runs in your terminal, reads and edits your actual project files, and runs commands itself, instead of only answering questions in a chat window.
Terminal-Native

Runs as a CLI inside your project folder, the same shape of tool as the Claude Code series already covered on this site.

Agentic

Reads files, edits them, and runs real commands to finish a task instead of only describing one.

Sandboxed

Runs inside a sandbox with configurable write access, a different safety model to review before trusting it on real infrastructure.

Introduction

This site already has a foundations series for Claude Code. Codex is OpenAI’s answer to the same category of tool, an agentic coding assistant that lives in a terminal rather than a browser tab, and it is different enough in its details, AGENTS.md instead of CLAUDE.md, approval modes instead of permission rules, that it earns its own series rather than a single comparison post.

This first post covers what Codex actually is and why it is worth an IT engineer’s time before installing anything. The rest of the series walks through setup, configuration, and the same daily-ops territory the Claude Code series covers.

What Is Codex?

Codex is an agentic coding tool built by OpenAI that inspects, edits, and runs code from your terminal while you stay in interactive control of what happens. It is one surface of a broader platform: the same underlying agent also runs as an IDE extension, inside a desktop app for coordinating projects, and in the cloud for tasks you want to kick off and check back on later.

Think of it the same way as the terminal colleague analogy from the Claude Code series: instead of describing a problem to a chat window and copying the answer out by hand, you describe a task to a tool that is already sitting in your project directory and can act on it directly.

Practical note: The first time you run Codex, it asks you to sign in with ChatGPT or another supported method. OpenAI’s own documentation recommends a Plus, Pro, Business, Edu, or Enterprise ChatGPT plan, or authenticating with an API key, which needs additional setup.

How This Differs From a Chatbot

The same distinction that matters for Claude Code applies here: what the tool is actually allowed to touch, not how well it writes.

Capability Typical browser-based AI chat Codex
Reads your files directly No, you paste content in manually Yes, reads the project directory itself
Edits files No, gives you text to copy out Yes, writes changes directly inside a sandbox
Runs commands No Yes, subject to the active approval mode and sandbox setting
Keeps project-specific context Limited to the current chat, re-pasted every time Persists via an AGENTS.md file read at the start of every run
Extends with custom procedures No Yes, through Skills, MCP servers, and delegated subagents
Key difference: A chatbot describes what to do. Codex can go ahead and do it, inside whatever sandbox and approval boundary you have configured for that session.

Where It Runs

The Codex ecosystem spans four surfaces built on the same underlying agent: the CLI, an IDE extension, a desktop app for coordinating several tasks, and Codex cloud for running work in a parallel environment you check back on later. For IT and ops work, the CLI is the natural starting point, the same reason the Claude Code series opened there.

Why an IT Engineer Would Want This

The pitch is the same one made for Claude Code, applied to a different vendor’s tool: reading real files and running real commands maps directly onto scripting, log triage, and documentation work, not just application development. Writing and debugging PowerShell or Bash is faster when the tool can see the actual script and read the actual error rather than working from a copy-pasted summary of it.

Where Codex earns a separate series rather than a paragraph in the Claude Code one is in the details: a different configuration file, a different approval and sandbox model, and its own take on skills, subagents, and MCP. An engineer who has to support both tools, or who is choosing between them, needs those details covered on their own terms.

What’s Coming in This Series

This post is Part 1. The rest of the series mirrors the shape of the Claude Code foundations series, with the specifics that are actually different between the two tools.

Part Topic
Part 2Installing Codex CLI, and fixing the install issues you are most likely to hit
Part 3Creating and navigating your first project
Part 4Understanding and writing AGENTS.md
Part 5Creating a custom Skill
Part 6Subagents in Codex
Part 7Approval modes, sandboxing, and config.toml
Part 8Common Codex issues and fixes, a standing reference
Part 9Connecting external tools with MCP

Final Thoughts

Codex is worth the setup time for the same reason Claude Code was: it does not stay a side conversation. It reads what is actually in front of it and acts on it directly, inside a sandbox you control.

The rest of this series exists to get you from a cold install to using Codex comfortably on real scripts and real infrastructure work, covering the parts that are genuinely different from Claude Code rather than repeating what the earlier series already established.

Key takeaway: If a tool can only answer questions in a chat window, it is a reference. If it can read your files, edit them, and run commands itself inside a sandbox you define, it is a colleague. Codex is built to be the second one.
Next in this series

Next, we install it: the native installer, npm, Homebrew, and Windows options, the account it needs, and the install-time errors engineers hit most often.