AI Coding Tools

AI coding tools went from autocomplete curiosity to everyday equipment in a few years, and the category now moves faster than most developers can track. Names change, products merge, and features that were experimental six months ago are suddenly standard. Rather than chase rankings, let’s sort the field by what the tools actually do, so you can place any new product the moment you see it.

A useful way to think about it is help in a kitchen. One helper hands you the right knife as you reach for it. Another takes a whole recipe and cooks it while you check in. A third delivers a finished meal and you never see the kitchen. Those are roughly our three types. Now for the precise version.

How AI coding tools work

Under the hood, almost all of these tools send context to a large language model and act on what comes back. The context might be the file you are editing, related files the tool searched for, error messages, terminal output or your written instructions. The model predicts code or a plan, and the tool turns that into suggestions, edits or commands.

The big differences between products come from three things. Which models they can use, how much of your project they can see and search, and how much they are allowed to do on their own: suggest only, edit files, run commands, or open pull requests. That last point, autonomy, is the best way to tell the types apart.

Type 1: In-editor assistants

These live inside your code editor and work alongside you as you type. They offer inline completions (gray suggested code you accept with a key press), a chat panel for questions about your code, and inline edits where you select code and describe a change. You stay in control of every line.

GitHub Copilot is the best-known example. It works as an extension for popular editors, and its documentation describes inline suggestions, chat, an agent mode for multi-file edits, and code review. At the time of writing, GitHub offers a free Copilot plan with limited usage alongside paid plans. JetBrains offers AI Assistant inside its IDEs, such as IntelliJ IDEA and PyCharm.

Some products are entire editors built around AI. Cursor is a desktop code editor designed for working with AI agents and for hands-on editing in the same place. Kiro, built by AWS, is an IDE based on Code OSS (the open source base of VS Code) and emphasizes spec-driven development, where the tool turns a prompt into requirements, a design and a task list before writing code. AWS has been moving users of its earlier assistant, Amazon Q Developer, toward Kiro.

Type 2: Agentic tools

Agents take a task, not a line. You describe what you want (“add input validation to the signup form and write tests for it”), and the tool reads the relevant files, plans, edits several of them, runs commands such as your test suite, and reports back. You review the result as a set of changes rather than approving each keystroke.

Terminal and desktop agents

Claude Code, from Anthropic, is an agentic coding tool that reads a codebase, edits files and runs commands; its documentation lists the terminal, IDE extensions, a desktop app and the web as places it runs. OpenAI’s Codex is available as a CLI, an IDE extension and a cloud service connected to a ChatGPT account. JetBrains’ agent, Junie, runs inside JetBrains IDEs and from the terminal, and left beta in 2026. Cursor and Kiro also offer CLIs that run their agents outside the editor.

Cloud agents

Cloud agents run tasks on remote machines, so the work continues when you close your laptop. GitHub Copilot’s cloud agent can be assigned a task and return the result as a pull request. Codex and Claude Code offer cloud or web sessions, and Cursor runs cloud agents that can be started from tools like GitHub and Slack. This model fits well with existing team habits, because changes arrive through the normal review process.

Type 3: App builders

App builders generate a complete application, usually a web app, from a description in a browser. They write the code, run a live preview and often handle hosting, a database and login. Many users never open the code, which makes these tools the home of what people call vibe coding. Our explainer on the vibe coding meaning covers where that approach works and where it does not.

Current examples include Lovable, Bolt.new (from StackBlitz), v0 from Vercel, and Replit Agent, which builds and deploys apps inside Replit’s online environment. They differ in which frameworks they generate, how much backend they set up and how easily you can export the code to run elsewhere. That last point matters more than it seems on day one.

How to choose among AI coding tools

The right tool depends more on your situation than on any leaderboard. Ask these questions before committing:

• Do you want suggestions you approve line by line, or delegated tasks you review afterward?
• Does it work in the editor and language you already use?
• How much of your codebase can it see, and can you control what it sends to the model?
• What does your employer or client allow regarding code leaving your machine?
• How is usage billed: flat subscription, credits, or per request?
• Can you export everything and leave without rewriting the project?

If you are a beginner learning to program, an in-editor assistant keeps you closest to the code, which is where learning happens. If you are an experienced developer, agents can take whole chores off your plate, provided you review what they produce. If you are not a developer and want a working prototype, an app builder is the shortest path, with the caveat that someone should review the code before real users arrive.

Try it

Let’s compare two levels of autonomy on the same task. Make a small project folder with one file containing a function that adds two numbers, and put it under Git so you can undo anything (our explainer on what Git is covers the three commands you need).

First, open the file in an editor with an AI assistant and ask it, in chat, to add input checks so the function rejects anything that is not a number. Read the suggestion and apply it yourself. Then reset with git checkout -- ., give the same request to an agentic tool, and ask it to also write and run a test. Compare what each one changed using git diff. You will learn more about how these tools behave from that diff than from any feature list.

Common mistakes

AI tools are fast at producing code, and we developers are fast at trusting fast things. A few habits keep that in check.

• Accepting large changes without reading the diff
• Letting an agent run commands without understanding what they do, especially anything that deletes or deploys
• Pasting secrets, credentials or customer data into prompts
• Skipping tests because the code “looks right”
• Asking for a fix without giving the tool the actual error message and context
• Choosing a tool by popularity rather than by whether it fits your editor, language and rules

The most useful habit is simple: commit before you ask an AI to change anything significant. If the result is wrong, you are one command away from where you started.

Where AI coding tools fit

AI assistants are now one category among the tools a developer relies on, alongside editors, version control, containers and hosting. Our guide to dev tools maps the whole set and shows how they connect.

What to learn next: AI tools are most effective when you understand what they are generating. A good place to start is what an API is, because a large share of real-world code is about calling one, and you will catch more mistakes in generated API code once you know how requests and responses work.