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

The working pattern for local vs cloud agent choice is small enough to review and specific enough to repeat. Give the agent a named task and only the context it needs, then tie the result to a check you can rerun. The table below breaks that into moves.

Each of those moves fails in its own particular way, and the order they come in is doing more work than it looks like it is. Skip the boundary and you get a diff nobody wants to read, because the agent has quietly rewritten files you never meant to open. Thin context fails more quietly: the code compiles, the tests pass, and it ignores every pattern the rest of the repo follows. Then there's the check, which to me is the real one, because it's the whole difference between a result you verified and a result you're taking someone's word for.

The step people skip is the plan. I think it's because it feels like overhead, thirty seconds of nothing visibly happening while you're trying to get work done. Actually, that's not quite the reason. It's that the cost of skipping it lands much later, so it never registers as the mistake it was. If the plan names files you didn't expect, you've learned something for free. If it names the right ones, you've got a reference to check the diff against when it arrives. And once there are four hundred lines on the screen, changing the approach means throwing that work away, which nobody is good at.

Running local vs cloud agent choice as a team comes down to one habit: leave a trail the next reviewer can follow. The steps below keep the prompt and its proof attached to the change, so nobody has to reverse-engineer what the agent did.

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.

Keep whatever lets you rerun the work or hand it to someone else. A finished task is the merged code plus the short trail that explains 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.

Think of it as an engineer with a laptop in the cloud. Cursor's products are just ways to orchestrate that cloud developer: through Slack, through Linear, or through the IDE. You hand off tasks into the background and let them run while you do other work. For non-engineers especially, getting cloud agents set up is a high-value early investment.

When you don't want to be chained to a long-running local task, or you aren't at your desk. From cursor.com/agents on any device (one presenter kicked off a run from a phone on the Bay Bridge) you pick a repository and branch, give a command, choose a model, optionally link 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 like Slack, and start it.

An environment, and it is the cost that keeps getting left out of the comparison. A local agent inherits your machine for nothing, because your dependencies and your credentials are already sitting there. Cloud agents run on isolated Ubuntu machines, so anything the code needs in order to install and run has to be configured before the first useful run.

Nobody counts that setup when they weigh the two.

Two paths to that. Let Cursor's agent set the machine up, which it can do in under 10 minutes while you watch in a shared terminal, then save a snapshot so later agents start from it. Or write a Dockerfile for system-level dependencies and reference it from .cursor/environment.json. Cursor recommends the first for most repos and treats the second as the advanced case.

The mistake worth naming up front is secrets. They live 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. Cursor's published schema for .cursor/environment.json has no secrets field at all, so a key pasted into that file isn't configuration, it's a plaintext leak committed to your repo.

Config resolution takes the first match: .cursor/environment.json in the repo, then a personal saved environment, then a team one. That ordering is what lets one person test a new config before it becomes everybody's default.

Because of what comes back with it. Each cloud agent runs in its own isolated VM with a full desktop environment, so 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 attach to that PR.

So the reason to reach for cloud isn't only the parallelism everyone leads with. It's that verification arrives as an artifact instead of as a claim, and you can validate the change from the PR without checking the branch out at all.

The clip below runs one handoff end to end.

There's an opt-in setting, Allow posting artifacts to GitHub, if you want them embedded in the PR description itself.

You can also take control of the agent's remote desktop, interact with the software it is building, and hand control back whenever you like. I suspect that's the part that settles it for people who don't trust a screenshot, though that's a guess about taste rather than anything measured.

Yes, though on the runtime rather than on the local-or-cloud question. In all three of Cursor's Cloud Agent runtimes the agent loop still runs in Cursor's cloud; what changes is where tool calls execute and who operates that execution environment.

Managed is the default, and Cursor calls it usually the lowest-operations way to give agents secure access to code and internal systems. My Machines is framed for personal or small-scale workflows on one user's machine. Self-Hosted Pool is for Enterprise teams that want centralized ownership of the 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. and controlled secrets.