AI Coding Agents: How to Choose One (From Someone Who Ships With Them Daily)
I build software with AI coding agents every day, and the 2,400+ tools on this site were built with them. The question I get most is "which one is best?" The honest answer depends on how you work, so here is how to choose, and the setup that matters more than the choice.
What an AI coding agent actually does
Autocomplete suggests the next line. An agent takes a task, like "add a CSV export to this page," reads the relevant files, plans the change, edits several files, runs your tests, and fixes what breaks. You review the result instead of typing every line. That shift, from writing code to deciding and checking, is the whole story of these tools.
The three kinds of coding agents
Terminal agents
Claude Code, OpenAI Codex CLI, Gemini CLI
Run in your terminal inside your project. They read files, run commands and tests, and make changes across the whole codebase.
Best for: Multi-file changes, refactors, and work you want to verify by running the tests.
Editor agents
Cursor, GitHub Copilot agent mode in VS Code
Live inside your code editor, so you see every change as it happens and can steer it.
Best for: Day-to-day coding where you want to watch and approve each edit.
Background agents
OpenAI Codex in the cloud, the GitHub Copilot coding agent
Work on a task on their own, in the cloud, and come back with a pull request.
Best for: Well-defined tasks you can hand off and review later, like small bugs and chores.
Most of these can do more than one of these jobs, and the lines keep blurring. Start from where you already work: the terminal, the editor, or GitHub.
What I use, and why
I run Claude Code and OpenAI's Codex on the same repository as peers. Both build, both review each other's work, and they coordinate through plain files and git. Two different agents catch each other's mistakes far more often than one agent checking itself. I wrote up the exact setup in how to make Claude Code and Codex work together. The workflow behind this site's tools is in how I built 2,400+ tools with AI agents.
What's the best AI model for coding?
Any answer goes out of date within months, because the leading labs ship new models constantly. Public leaderboards like SWE-bench Verified, which tests models on real GitHub issues, give a rough signal. They don't tell you how a model handles your codebase, your conventions, and your tests. The test that actually works:
- Pick one real task from your backlog, something that touches a few files.
- Give the same task to two or three agents, each in its own branch.
- Compare the results. Does it run, pass the tests, follow your style, and avoid touching things it shouldn't?
An hour of that tells you more than any leaderboard.
What about Python?
Every major agent handles Python well, so the language rarely decides it. The differences show up in how well an agent understands the whole project, runs your tests, and sticks to your conventions.
AI code review tools
Review is where AI pays off quietly. Tools like GitHub Copilot code review and CodeRabbit comment on pull requests and catch real bugs. My favorite trick costs nothing extra: ask a different agent to review the first agent's work. Treat either kind as a second pair of eyes, not a replacement for a person who understands the change.
The setup matters more than the choice
- A project config file. CLAUDE.md or AGENTS.md tells the agent your conventions, commands, and rules. Here is what to put in them.
- Tests the agent can run. An agent that can check its own work fixes most of its own mistakes.
- Small, clear tasks. "Add a CSV export to the invoices page" goes far better than "improve the app."
- A human review before anything ships. Agents sound just as confident when they're wrong. I cover the common failure modes in what AI coding agents are still bad at.
How to choose, in one list
- Live in the terminal? Try a terminal agent.
- Want to watch every edit? Use an editor agent.
- Have a backlog of small, clear tasks? Try a background agent that opens pull requests.
- On a team? Check how each tool handles your code and data, and pick one that fits your pull request review process.
Frequently asked questions
What is an AI coding agent?
An AI tool that does more than suggest the next line. It reads your project, plans a change, edits files, runs commands and tests, and fixes what fails, with you reviewing the result.
What is the best AI model for coding?
It changes every few months as new models ship, so any single answer goes out of date fast. Public benchmarks like SWE-bench Verified give a rough signal, but the reliable test is running two or three agents on the same real task in your own codebase and comparing the results.
What is the best AI for Python coding?
Every major coding agent handles Python well, so the language is rarely the deciding factor. The differences show up in how well an agent understands your whole project, runs your tests, and follows your conventions.
Are AI code review tools worth it?
Yes, as a second pair of eyes, not a replacement for human review. Tools like GitHub Copilot code review and CodeRabbit catch real bugs. Asking a second agent to review the first one’s work catches even more.
Is Claude Code or Codex better?
I use both on the same repo, and which one is better changes with each release. They also catch each other’s mistakes, which is the main reason I keep both.
Need something built with AI?
I build custom AI features, internal tools, and MVPs with this exact workflow. Tell me what you need. The first consult is free.
See custom AI development