Terminal agent or AI editor? Picking between Claude Code and Cursor

Claude Code and Cursor overlap more than their interfaces suggest. Both can read a repository, edit several files in one go, run commands and explain code you did not write. The difference that lasts is where you sit while the work happens. Claude Code is Anthropic's coding agent, used mainly from the terminal. Cursor is an AI code editor, so the agent works inside the same window you type in. Feature lists on both sides change often; the way you like to work changes slowly. This guide compares the two shapes on workflow, permissions and review, then gives you a checklist for deciding, including the option of using both.

Two shapes of the same idea

A terminal agent starts from a task description. It reads files, runs commands, edits the working tree and reports back in a transcript. You can watch every step, but the natural rhythm is to hand over a chunk of work and review the result. An editor agent lives in the editing surface: completions as you type, inline edits on a selection, a chat panel, and an agent mode that changes several files and shows the diffs where you are already looking.

Neither shape is more capable in principle. Each makes different behaviour easy, and that is what you are really choosing. If the vocabulary of agents, tools and harnesses is unfamiliar, what an agent harness is gives the background.

Workflow fit: what fills your week

Look at the last two weeks of your own work and sort it. Some tasks are delegation-shaped: moving call sites to a new API, writing tests for an untested module, upgrading a dependency, chasing a failing build, chores that need several commands in sequence. These suit a terminal agent, because you can describe the goal, let it work and review the outcome as one change.

Other tasks are steering-shaped: building a component while you look at it, refactoring a function you are actively reading, trying three variants of a query. Here an editor agent fits better, because each change appears in place and you can correct course without switching windows.

Three practical factors often settle it:

• Your editor. Adopting an AI editor means moving your daily editing into it. If you or your team are attached to another editor, a terminal agent sits next to whatever you already use.
• Where the code runs. Work on remote servers, inside containers or across several repositories at once is often easier from a shell.
• Automation. If you want the same agent to run in scripts or pipelines, check whether the tool offers a non-interactive mode and how it reports results and failures there.

Permissions: what the agent may do without asking

Both kinds of tool can edit files and run commands, so the permission model matters more than the interface. For each candidate, check:

• Does it ask before running a shell command, and can you approve a specific command once or for the rest of the session?
• Can you allowlist routine commands such as the test runner and linter, and deny others outright?
• Where do these settings live: per user, per project in a file you can commit, or managed centrally for a team?
• Is there a mode that approves everything, and can a team switch it off?
• Can the agent read secrets in the working directory, such as environment files, and how do you exclude them?

Write the policy down before you install anything. A starting point for a typical repository:

allow:  read files, run unit tests, run the linter, show git status and diff
ask:    install or upgrade a dependency, edit CI configuration, run migrations
deny:   read .env and key files, push to a remote, delete outside the repo,
        call arbitrary hosts from the shell

Then configure each tool to match it and see which parts it can express. The habit of granting less first is covered in start agents read-only.

Review habits decide the quality

The tool proposes; you decide what merges. Each shape has its own failure mode. In an editor, accepting a diff is one click, which makes it easy to approve changes you only skimmed. In a terminal, the agent can produce a large change in one run, and a long diff invites the same skimming.

A few habits help with either:

1. Ask for a short plan before any edit, and reject plans that touch more than the task needs.
2. Write the acceptance criteria first: which tests must pass, which behaviour must not change, which files are out of scope. Writing acceptance criteria before the agent starts explains why this order works.
3. Keep one task per branch, so a bad run is cheap to throw away.
4. Read the diff as if a new colleague wrote it, then run the tests yourself.
5. Keep commits and pushes in your own hands until you trust the setup.

Extending the tool

Both tools can be extended, and this is where they converge. Check how each one connects to MCP servers, how it loads project instructions from a file in the repository, and whether those instructions can be shared with the team through version control. Extensions widen what the agent can touch, so the permission questions above apply to every server you add. For how MCP servers, skills and plain CLI tools compare as extension points, see MCP, skills or CLI tools. If you are weighing several terminal agents against each other, the guide to comparing terminal coding agents on your own repository sets out a trial method.

Where GoCux fits

GoCux is neither a coding agent nor an editor, and it does not replace either. It becomes relevant when development work has to be reviewable by more people than the developer at the keyboard. A work order states the goal, the scope the work may touch and what counts as done, and each step lands on one record as it happens. Actions with consequences, such as sending something out, deleting something or exporting data, stop and wait for a decision from someone on the team; the seven action classes list what pauses. The customer CLI keeps control of its own local tools and permissions. Development work with a reviewable result shows how this applies to code.

A decision checklist

• Most of your agent work is delegation-shaped: lean towards a terminal agent.
• Most of it is steering-shaped, done while you read the code: lean towards an editor agent.
• You will not change editors: a terminal agent avoids the question.
• You need scripted or pipeline runs: confirm a non-interactive mode exists and behaves well on failure.
• Your written permission policy can be expressed in the tool's settings and shared with the team.
• Secrets in the working directory can be excluded.
• Your review habit is defined: plan first, acceptance criteria, one branch per task, tests run by you.
• You trialled each candidate for a week on real tasks from your backlog and noted where your attention went.