What is the difference between an AI coding agent and autocomplete
An AI coding agent and autocomplete differ in this: autocomplete follows the line you are typing, while an agent takes a task you have written down and edits files on its own. You can reject autocomplete, or accept only part of it. Files an agent changes, and commands it proposes, still wait until a person has looked at them.
On this page
Autocomplete follows the cursor
Autocomplete appears where you are typing and suggests the next line, or a small edit. You accept it, reject it, or keep typing so it drops away. It does not decide which files this task should change.
That layer fits when you already know the place and only the wording is open. If the task itself is not written down, autocomplete can still offer a fragment that looks complete. Whether the fragment enters the repository depends on whether it matches the change you meant.
An agent takes the whole task
An agent starts from a task you wrote down, lists the files to read, the edits to make, and the commands to run, then repeats until it treats the task as done. Read the whole diff and the command record. The last sentence that says the work is finished is not the review.
Hand over work that has several steps, that has to stay consistent across files, or that needs a command before you can tell whether it is right. A change inside one function you already have open is usually autocomplete. How an agent sits in a workflow is covered under Workflow agents.
Cursor separates Tab from Agent
The Cursor Tab page describes Tab as autocomplete from your recent edits, the surrounding code, and linter errors. Suggestions show as grey text ahead of the cursor. Accept the whole suggestion with Tab, reject it with Escape or by typing, or accept it word by word. That page says that after you accept once, another Tab predicts the next edit location and jumps there, and that a jump to another file appears at the bottom of the editor.
The Cursor Agent page describes Agent as an assistant that can complete complex coding tasks, run terminal commands, and edit code. The same page says Agent creates a checkpoint before significant changes, stored locally and separate from Git, and that restoring it reverts files only. Lasting history is still a commit in Git.
GitHub Copilot separates the two modes
The responsible-use page for inline suggestions describes those suggestions as autocomplete: finish the current line, generate a new block, or propose an insert, a modification, or a deletion. You can accept all or part, dismiss them, or keep typing. That page says syntactically correct code can still be insecure, and that secure-coding and review practice still apply.
The page on agent mode in the IDE says to use agent mode when the task is complex, has several steps and error handling, and you want Copilot to choose the steps. It decides which files to change, offers code changes and terminal commands, and iterates until the original task is complete. Commands wait for approval by default. The same page says that if a task is large or ambiguous, draft an implementation plan first. How to choose the product is covered in How to choose between Cursor and GitHub Copilot.
A large task starts as a plan
The Copilot agent-mode page suggests drafting an implementation plan when the task is large or ambiguous. The plan names the behaviour that should change, the behaviour that should stay, and how completion will be checked. The agent follows the plan. A spoken “just fix it” is not a plan.
A person responsible for the work reads the plan before the agent receives it. If the agent edits a file the plan did not name, the review sends the whole diff back. A person removes the extra edits before it is sent again.
A person still accepts the diff and the command
Accepting autocomplete happens beside the cursor, where the person typing can see the suggestion at once. Accepting an agent happens after a batch of files and some commands, and needs a separate look at the diff. Do not treat those two accepts as the same habit.
If a command is allowed to run on its own, limit that list to commands already written down whose failure does not change data. Installing a package, pushing to a remote, or changing stored data still stops for a person. The Copilot page says an administrator or an editor setting can relax the default approval. A relaxation belongs in the project scope, with the name of the person who allowed it.
When autocomplete is enough
Autocomplete is enough when the task already sits in one open file, the edit can be read in a glance, and you do not need a command to know whether it is right. An agent will spread a small edit across more files, and the review then costs more than the edit.
If the repository has no review rule yet, or a directory still holds a secret that should not be read, leave the agent off. Draw the scope first. That is covered in How to connect an AI coding tool to an existing codebase.