What Is an AI Coding Agent?

Give the agent one named task, only the context it needs and a check you can rerun. A task shaped this way produces a diff small enough to review and a result you can repeat.

Each move fails in its own way. Without a boundary, the agent edits files you never meant to open, and the diff becomes hard to read. Thin context fails more quietly: the code compiles and the tests pass, but the change ignores patterns the rest of the repo follows. Without a check you can rerun, you are taking the agent's word that the work is done.

Asking for a plan before the agent writes code is the easiest step to skip. It means a short wait while nothing visible happens, and the price of skipping it shows up later, in review.

If the plan names files you didn't expect, you learn that before any code exists. If it names the right files, you have a list to check the diff against. Once four hundred lines of diff are on screen, changing the approach means throwing that work away.

The habit that matters most for a team is leaving a trail the next reviewer can follow. The steps below keep the prompt and its proof attached to the change.

1. 1Pick one real backlog item with a clear owner and expected result.
2. 2Add only the context the agent needs: files, failing output, constraints and done state.
3. 3Ask for a plan before code when the task touches more than one file.
4. 4Run checks that match the risk: unit test, typecheck, visual pass or review checklist.
5. 5Capture the prompt, diff, result and reviewer note so the workflow can be repeated.

Match the effort to the change. A one-file fix needs a clear done state and a typecheck, and a plan adds little. A change across several files is where a plan pays for itself, and where the check should include tests and a reviewer who knows that code.

Keep whatever lets someone rerun the work or pick it up later. A finished task is the merged code plus a short trail that shows how it got there.

• The prompt or plan that shaped the work.
• The files changed and the reason each file changed.
• The command, screenshot or review note that proved the result.
• The rule, checklist or template you would reuse next time.

The last item pays off over time. A rule or template saved from one good run means the next run starts from what already worked, not from a blank prompt.

Cursor's docs describe an agent as three components: instructions, tools and a model. The instructions are the system prompt plus your rules. The tools cover file editing, codebase search, terminal execution and more, and the model is the one you pick for the task. Cursor tunes the system prompt and the tools for each model it supports.

That leaves two parts in your hands: the model you choose and the rules you write.

Put your effort into the rules. They decide whether a given model writes code your team recognizes as its own, and they carry over unchanged when you switch models.

The tools are Cursor's to tune, but what they may reach is yours to decide. In our view, that permission matters more than the full list of what an agent could reach in principle. Run Modes, described below, set that limit for shell commands, MCPModel Context Protocol. A standard that lets an AI agent pull in context from outside the repo, like Jira tickets or internal docs. Press Enter for the full definition. tools and Fetch calls.

It means one prompt can lead to many actions. Cursor's docs say there is no limit on the number of tool calls an agent can make during a task. In a single turn it can search your codebase, read files (images included), edit them, run shell commands, drive a browser and ask you a clarifying question. While it waits for your answer, it keeps reading, editing and running commands.

Answer its questions quickly. Until your reply arrives, the agent keeps working on its current assumptions, so a slow answer means more work built on a guess.

All of this leaves a trail of actions for you to review. Read the diff before the agent's summary, because the summary is the agent's own account of many small decisions and the diff shows which of them reached your repository.

Checkpoints exist for runs that go wrong. Before it makes significant changes, Cursor automatically snapshots the files the agent modified. You can preview any point in the chat timeline and restore your files to that state.

Checkpoints are stored locally and kept separate from Git. Cursor's guidance is to use them only for undoing agent changes and to use Git for permanent version control. Treat a checkpoint as an undo for agent edits, and commit anything you want to keep.

Modes set how much autonomy you hand over for a particular task. Plan Mode researches the codebase, asks clarifying questions and produces a plan you can edit before anything is built. ⇧+⇥ rotates to it, and Cursor suggests it on its own when your wording points to a complex task. Use it when a wrong first step would be expensive, because you can correct the plan before any file changes.

Debug Mode shows how a mode shapes autonomy. It generates hypotheses and adds log statements that report to a local debug server running in a Cursor extension. Then it stops and asks you to reproduce the bug, using steps it gives you, which puts a human step in the middle of the loop by design. Afterward it reads the real logs and makes a targeted fix, and once you confirm the fix works, it removes its own instrumentation.

Run Modes sit underneath these modes. They decide when a shell command, MCPModel Context Protocol. A standard that lets an AI agent pull in context from outside the repo, like Jira tickets or internal docs. Press Enter for the full definition. tool or Fetch call runs on its own and when Cursor stops to ask you, and they can send shell commands into a sandbox that blocks unauthorized file access and network activity. Because the Run Mode decides what happens without you, set it before the first task.