Cursor Cloud Agents — autonomous coding that ships while you sleep

TL;DR: Cursor Cloud Agents are autonomous AI developers that run in isolated cloud VMs. They clone your repo, install dependencies, write code, run tests, interact with the UI, and open merge-ready PRs—with videos and screenshots proving the work. Set up your development environment well, and they work like engineers who never need a laptop.

What are Cursor Cloud Agents?

Cursor Cloud Agents take the same agent fundamentals you use in the IDE—tool calling, codebase search, multi-step planning—and run them in isolated virtual machines in the cloud instead of on your local machine.

Each agent gets:

• A dedicated Linux VM with a full terminal, browser, and desktop
• Your cloned repositories with dependencies installed
• Configured secrets, environment variables, and network access
• The ability to build, test, and interact with the software it changes

You describe a task in natural language. The agent works independently—whether you’re at your desk, in a meeting, or asleep—and delivers a pull request when it’s done.

How they differ from local agents

Local agents are great for pair programming. Cloud agents are for delegation—handing off entire tasks and reviewing the result later.

The agent loop in the cloud

A typical cloud agent run looks like this:

1. Onboard — Clone the repo, read AGENTS.md, install dependencies, run startup commands
2. Plan — Break the task into steps based on your prompt and codebase context
3. Implement — Write code, create branches, commit incrementally
4. Verify — Run linters, unit tests, builds, and UI-based checks
5. Demonstrate — Record video, capture screenshots, attach logs to the PR
6. Ship — Push the branch and open a merge-ready pull request

Because the agent has its own machine, it can close the loop on its own work—the same way a human engineer would.

Artifacts change how you review code

The most underrated feature is evidence. When a cloud agent finishes, the PR includes:

• Screen recordings of the agent using the software it built or fixed
• Screenshots of key UI states
• Logs from builds, tests, and runtime checks

Instead of reading a diff and imagining whether the login form works, you watch a 30-second video of the agent logging in. Review becomes faster and more confident—especially for UI-heavy changes.

You can also take over the agent’s remote desktop to click around yourself, then hand control back for the agent to keep working.

Setting up development environments

Agents are only as capable as the environments they run in. An agent that can write code but can’t run tests or reach your API cannot verify its own work.

Think of environment setup as giving your agent a computer:

# Example: install script for an Astro blog
pnpm install
pnpm run build
pnpm run dev  # agent can hit localhost:4321 for UI testing

Key configuration in the Cloud Agents dashboard:

• Repository access — GitHub, GitLab, Azure DevOps, or Bitbucket
• Secrets — API keys, tokens, environment variables (scoped per environment)
• Startup commands — Install deps, start dev servers, seed databases
• Network access — Allow agents to reach staging APIs or internal services
• Dockerfiles — Custom base images for specialized stacks

Cursor’s docs put it plainly: not setting up a development environment for your cloud agents is like not giving your engineers a computer.

Multi-repo and enterprise workflows

Recent updates add multi-repo environments—configure one agent session with frontend, backend, infrastructure, and shared-library repos. The agent can make coordinated changes across services and open PRs in each repo it touches.

For teams with strict security requirements, self-hosted cloud agents keep code and tool execution entirely in your own network while preserving the same VM-based workflow.

Where to launch agents

You can start cloud agents from:

• Cursor Desktop — Delegate from the agent panel
• cursor.com/agents — Web dashboard for monitoring runs
• GitHub — Comment @cursor on issues and PRs
• Slack / Microsoft Teams — Mention @Cursor with a task
• Linear, PagerDuty, webhooks — Via Automations
• Mobile — Kick off tasks from your phone

Automations let you run agents on a schedule or in response to events—nightly dependency updates, CI failure triage, or new-issue auto-investigation.

Practical use cases

Good fits:

• Bug fixes with clear reproduction steps
• Feature implementation with defined acceptance criteria
• Writing tests for existing code
• Dependency updates and migration chores
• Documentation updates tied to code changes
• Parallelizing independent tasks across multiple agents

Less ideal:

• Ambiguous product decisions that need human judgment
• Tasks requiring credentials you can’t safely provide
• Deep architectural debates without written constraints

Tips for better results

1. Write clear task descriptions — Include acceptance criteria, file hints, and what “done” looks like
2. Maintain AGENTS.md — Project conventions, build commands, and testing instructions onboard agents faster
3. Configure hooks — Cloud agents run command-based hooks from .cursor/hooks.json
4. Use MCP servers — Give agents access to databases, APIs, and third-party tools
5. Start small — Pilot on well-scoped tasks before delegating complex refactors
6. Review artifacts first — Watch the demo video before diving into the diff

The bigger picture

At Cursor, cloud agents already account for a significant share of merged PRs internally. The trajectory is clear: agents that don’t just write code, but build, verify, and demonstrate that code works—then ship it for human review.

Cloud agents are not a replacement for engineering judgment. They are a force multiplier for teams that invest in good environments, clear task descriptions, and review workflows that leverage visual proof.

Start with one well-defined task. Configure your environment. Delegate. Review the PR with its attached demo. That’s the loop—and it works.