Coding agents explained: Claude Code, Cursor, Codex and how to choose

A coding agent takes a goal, reads your codebase, edits files, runs commands or tests, and comes back for review. Autocomplete only suggests the next line while you type. Claude Code is terminal-first, Cursor lives in an AI-native editor, and OpenAI Codex runs across a CLI, IDE, cloud and web. Pick by the surface you use, then review every diff. A coding agent takes a goal, reads your codebase, edits files, runs commands or tests, and comes back for review. Autocomplete only suggests the next line while you type. Claude Code is terminal-first, Cursor lives in an AI-native editor, and OpenAI Codex runs across a CLI, IDE, cloud and web. Pick by the surface you use, then review every diff.

This is the question Reddit threads in r/cursor, r/vibecoding and r/AIAgentsStack have been asking all year: not "which model is smarter," but "which tool fits how I actually work." The products have converged on the same loop. The differences that remain are where the agent runs, how you watch it, and how you keep it from touching the wrong files.

What is a coding agent?

Anthropic's Claude Code overview calls it "an agentic coding tool that reads your codebase, edits files, runs commands, and integrates with your development tools." Cursor's docs describe Cursor as "a coding agent for building ambitious software" that can understand a repo, plan and build features, fix bugs and review changes. OpenAI's Codex CLI documentation says Codex can inspect files, make edits and run the tools already installed on your machine.

The shared idea is a loop, not a single completion: gather context, take action, check the result, and repeat. You set the goal and review the outcome. The agent chooses many of the in-between steps. That is the difference from a chat window that only talks about code, and from inline assistants that only see the current file.

How is that different from GitHub Copilot or autocomplete?

Older assistants suggested the next line or function as you typed. An agent can take a larger brief — "add pagination to the users endpoint and write tests" — then inspect files, edit across the project, run the test suite and report back. GitHub Copilot has also grown an agent mode that works on GitHub.com; the important distinction is the workflow, not the brand name. If you are still accepting one-line suggestions, you are using an assistant. If you are handing off a multi-file task and reviewing a diff, you are using an agent.

Claude Code vs Cursor vs Codex: how should I choose?

Our practical guide is Cursor vs Claude Code vs Codex: how to pick an AI coding agent. The short version, based on each vendor's own docs and on how people describe their setups:

• Cursor is editor-first. The agent lives in an AI-native editor, so you see and accept changes in context. That suits interactive, visual work and developers who want to stay in the file.
• Claude Code is terminal-first, with additional IDE, desktop and web surfaces in Anthropic's overview. It is the usual pick in community threads for longer, multi-file refactors you kick off and review later.
• OpenAI Codex is multi-surface: CLI, IDE, cloud and web, and it sits alongside ChatGPT rather than as a standalone editor. Cloud tasks can come back as changes you review and open as a pull request.

Model quality is close enough, and changes often enough, that workflow fit matters more than last week's benchmark. If you want to avoid lock-in, keep project instructions in an AGENTS.md file and reusable steps in agent skills, which several tools can read.

Community threads also keep returning to usage limits and to a "plan with one tool, implement with another, review with a third" pattern. Those are user reports, not vendor specifications, and plan prices change, so confirm the current plan page before you budget.

What is MCP, and what is AGENTS.md?

MCP, the Model Context Protocol, is an open standard that connects an AI app to outside tools and data through one interface. A connected server can reach real systems such as your database, so start read-only and keep approvals on. The longer explainer is What is MCP?; the safety companion is MCP security best practices.

AGENTS.md is a plain Markdown file in your repo that tells coding agents how to work on it: commands to run, rules to follow, and what "done" looks like. Codex, Cursor and GitHub Copilot read it. Treat it like a README for agents, and keep it short and concrete.

How do I run a coding agent safely?

Decide three things before you start: what the agent can touch (the sandbox), when it must ask (the permission or approval mode), and what is never allowed (deny rules). Keep secrets in environment variables, not in the prompt or in a file the agent can read. Save full-bypass modes for isolated containers or VMs. Auto or default-approve modes reduce prompts; they do not make the run safe. The full checklist is in how to run coding agents safely. Also read prompt injection for agent builders, because hidden text in a webpage or ticket can hijack a tool-using agent.

Whatever the agent writes still needs a human review before production. Use the review checklist, and keep the work in git so you can revert a bad run (git basics for vibe coders).

Sources

• Claude Code, Overview
• Cursor documentation
• OpenAI, Codex CLI
• AGENTS.md official site
• Model Context Protocol, architecture overview
• Claude Code, permission modes
• OpenAI Codex, sandboxing and approvals
• Reddit threads used only to identify questions, not as sources for figures or prices: r/cursor, Cursor vs Codex vs Claude Code, r/vibecoding, when to use each, r/AIAgentsStack, what people use in production