Cursor Agent isn't an autocomplete upgrade. It's a separate mode that can open files, run shell commands, search the web, take screenshots of a live browser, and execute dozens of tool calls in a row without stopping to ask you to confirm each one. When you open it with Cmd+I on Mac or Ctrl+I on Windows, you get a side pane where you describe what you want built or fixed, and Agent goes off and does the work. The interesting part isn't that it writes code — it's everything else it does along the way.
What Agent is actually made of
Before getting into how to use it, it helps to know what's running under the hood. Agent is built on three components that work together: Instructions, which are the system prompt and any rules you've written in a .cursorrules file or Cursor's settings; Tools, which are the actual capabilities Agent can invoke during a task; and a Model, which is whichever AI is doing the reasoning — Claude, GPT-4o, or others depending on your settings.
You don't need to configure any of this to get started. The defaults work. But knowing the structure matters because it tells you where to look when Agent behaves unexpectedly: if it keeps making the wrong assumptions, the issue is probably in Instructions; if it can't do something you expected, it's probably a Tools limitation; if the output quality is off, try switching the Model.
The tools Agent can actually use
This is the part most people don't fully appreciate until they watch Agent work through a real task. It isn't limited to reading and writing files. Here's what it has access to.
Semantic search means Agent can find code by meaning, not just by matching exact words. So if you say “find where we handle authentication errors,” it won't just scan for that exact string — it looks for code that does that thing, even if the variable names are completely different. Semantic, here, just means meaning-based: it understands intent rather than matching characters. This is more like asking a colleague who knows the codebase than running a text search.
File and folder search lets Agent locate files by name or structure across the whole project. It can also read files including images — so if your project has a screenshot of a UI, Agent can look at it and use that context when writing code.
Web search means Agent can look things up mid-task. If it hits an unfamiliar API, a package it hasn't seen before, or an error message it needs to understand, it can search for that information and keep going. You don't have to pause the task and go look things up yourself.
Shell commands are a big one. Agent can run commands in the terminal — install packages, run tests, build the project, check outputs. If it writes a new function and wants to verify the tests still pass, it can just run them. No copy-paste required.
Browser control is the one that surprises most people. Agent can open a browser, take a screenshot of what's rendered, click elements, and verify visual changes. So if you ask it to fix the layout of a component, it can actually look at the result in a real browser rather than guessing from code alone.
Image generation is available too — Agent can generate images and save them directly into your assets folder, which is handy when you need placeholder images or icons while prototyping.
And then there's the way Agent handles clarifying questions, which works differently than you'd expect. When Agent needs to ask you something, it doesn't stop and wait. It sends the question and keeps working in parallel, using its best guess in the meantime. When you answer, it incorporates your input. A long task doesn't grind to a halt just because Agent hit an ambiguous decision.
There's no cap on how many tool calls Agent can make in a single task. If building the feature requires twenty file edits, three shell commands, and a web search, it'll do all of that without you having to stage each one.
The checkpoint system — your safety net
A checkpoint is an automatic snapshot of your project state, taken by Agent before it makes a significant change — restructuring a component, deleting files, rewriting a module. These snapshots are stored locally, separate from Git, and exist purely so you can undo what Agent did if it went in a direction you didn't want.
You can preview what a checkpoint contains and restore to it at any point. Git is still for permanent version control and collaboration. Checkpoints are just for the “wait, go back” moments. More like a private undo history for AI changes specifically than anything else.
This is worth knowing before you start a big task. You can give Agent a genuinely open-ended instruction — “refactor the data-fetching layer to use React Query” — and if it goes sideways, you step back to the last checkpoint and try a different angle. Nothing is permanent until you commit it.
Staying in flow while Agent works
Most AI coding tools make you wait. Agent works, you wait, it finishes, you respond. Cursor Agent handles this differently, and it changes how a working session actually feels.
While Agent is mid-task, you can type your next instruction into the pane and press Enter to queue it. Queued messages execute in order once Agent finishes what it's currently doing. You can reorder queued messages by dragging them, so if you think of something more urgent while waiting, you can bump it to the front without retyping anything.
If you need to cut in right now — say Agent is going down a path you want to redirect — Cmd+Enter on Mac or Ctrl+Enter on Windows sends your message immediately, appended to the most recent instruction, and Agent processes it without waiting for the current task to finish. This is the interrupt. Use it when the direction needs to change.
The practical effect is that you stay in motion. While Agent is building one thing, you're already composing the next instruction. The session feels continuous rather than a rhythm of waiting and responding.
How to actually start a task
Open Agent with Cmd+I or Ctrl+I. Describe what you want built or fixed — as specifically or as loosely as you like. Product terms work fine: “add a way for users to save their preferences and have them persist across sessions.” Agent figures out the technical path from there.
You don't need to point it at specific files or tell it which commands to run. It will find the right files using semantic and file search, make the edits, run any necessary shell commands, check the results, and ask if it hits something genuinely ambiguous — all without stopping the work.
Once it finishes, review the changes in the diff view. If something looks off, send a follow-up instruction in the same pane, or restore a checkpoint to step back. If everything looks right, commit to Git as you normally would.
After the first few sessions, most people stop thinking about the tools and the queuing and the checkpoints as separate things to manage. You describe the goal. Agent works. You watch, redirect occasionally, and ship. It's less like prompting an AI and more like pairing with a very fast collaborator who just needs you to tell them where to go.