Cursor Local Agent vs Cloud Agent: Which to Use (2026)

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.

A presenter in Cursor's workshop for product managers described a cloud agent as an engineer with a laptop in the cloud. You can direct that engineer from Slack, Linear or the IDE. You hand off tasks to run in the background while you do other work. For product managers, the same presenter called setting up cloud agents early one of the most useful investments they can make.

Cloud wins when you don't want to be tied to a long-running local task, or when you aren't at your desk. In one Cursor workshop, the presenter kicked off a run from a phone on the Bay Bridge. At cursor.com/agents, on any device, you pick a repository and branch, write the instruction, choose a model, link any 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. servers such as Slack, and start the run.

An environment, which is easy to leave out of the comparison. A local agent inherits your machine for free, because your dependencies and credentials are already there. Cloud agents run on isolated Ubuntu machines, so anything the code needs to install and run has to be configured before the first useful run.

There are two ways to set one up. Cursor's agent can do it for you in under 10 minutes while you watch in a shared terminal, and Cursor saves the environment once a Build succeeds, so later agents start from it. Or you can write a Dockerfile for system-level dependencies and reference it from .cursor/environment.json. Cursor recommends the agent-driven setup and calls the Dockerfile route the advanced option.

Put secrets in the Secrets tab of the Cloud AgentsAgents that run in a Cursor-managed virtual machine, check out the repo, do the work and open a pull request, then shut down, with no load on your laptop. Press Enter for the full definition. dashboard, where the agent reads them as environment variables. Never paste one into .cursor/environment.json. The schema Cursor publishes for that file has no field for secrets, and the file lives in your repo, so a key pasted into it is a plaintext secret committed to version control.

When more than one configuration exists, Cursor uses the first match: a personal saved environment, then a team saved environment, then .cursor/environment.json in the repo for single-repo runs. Because the personal environment comes first, one person can try a new configuration before it becomes the team default.

Because of what comes back with it. Every cloud agent gets its own isolated VM with a full desktop environment. It can start a dev server, drive a browser with mouse and keyboard, click through a UI flow and check the change before it opens a pull request. The screenshots, videos and log references it produces are attached to that PR.

Parallel runs are one reason to use the cloud. The evidence is another: verification arrives as an artifact, and you can validate the change from the PR without checking out the branch.

The clip below follows one handoff from start to finish.

To embed the artifacts in the PR description itself, turn on the opt-in setting "Allow posting artifacts to GitHub". Cursor's docs note that embedded artifacts then sit at long, unguessable URLs that anyone with the link can view, because GitHub's image proxy needs public URLs.

You can also take control of the agent's remote desktop, use the software it is building, and hand control back at any time. If a screenshot is not enough evidence for you, take control and test the change yourself.

Yes, though the difference shows up in the runtime, not in the choice between local and cloud. In all three Cloud Agent runtimes, the agent loop runs in Cursor's cloud. What changes is where tool calls execute and who operates that environment.

Managed is the default, and Cursor describes it as usually the lowest-operations way to give agents secure access to code and internal systems. My Machines suits personal or small-scale workflows on one user's machine. Team Pools are for Enterprise teams that want centralized ownership of worker hardware. All three support Privacy ModeCursor's setting that guarantees code data is not used for training by Cursor or its model providers, and that an admin can enforce org-wide; data-retention terms are a separate, contractual layer. Press Enter for the full definition., but they handle secrets differently: a My Machines worker uses the machine's own credentials.