Cursor Background Agents: Review AI Changes Before You Merge

What Happened?

Cursor has expanded its coding workflow beyond the editor. Its current documentation describes background or cloud agents that can work on a repository in an isolated remote environment, run commands, make changes, and prepare those changes for handoff or review. The practical change is important: a coding task can continue after you close the editor, while the result arrives as a reviewable set of changes.

Cursor says background agents can clone a GitHub repository, work on a separate branch, push changes, and create pull requests. Its newer Cloud Agent documentation also describes long-running agents that can build features, fix bugs, write tests, and attach artifacts such as logs or demonstrations to the resulting pull request. See the Cursor Background Agents documentation at https://docs.cursor.com/background-agent and Cursor Cloud Agents at https://prod.cursor.com/help/ai-features/background-agents.

That does not mean the agent output should be treated like a merge-ready patch. The useful mental model is closer to a remote junior engineer who can work quickly: the agent can do substantial implementation work, but the repository still needs a review gate.

Why People Are Talking About It

The attraction is easy to understand. A developer can describe a task, let the agent work remotely, and return later to inspect the result. Cursor also supports web and mobile handoff, which makes asynchronous work practical when the developer is away from the desktop. The documentation says collaborators can review diffs, provide feedback, and manage pull requests from the web interface. See https://docs.cursor.com/en/background-agent/web-and-mobile.

That convenience changes where engineering effort moves. Instead of spending all the time typing code, developers spend more time defining the task, constraining the agent, checking its assumptions, and validating the final diff.

A Reddit discussion about Cursor raised a different but related concern: a user reported that an agent generated code that appeared very similar to a closed-source competitor's approach. That post is a user report, not proof that Cursor copied code or that any infringement occurred. The useful lesson is narrower: when generated code touches proprietary algorithms, security-sensitive logic, licensed material, or unusual implementations, reviewers need to understand where the code came from and why it belongs in the project. See https://www.reddit.com/r/cursor/comments/1u8gsyv/i_think_the_agent_just_generated_proprietary_code/.

What Users Experienced

The current Cursor workflow is designed to make agent work inspectable. Background agents run in isolated Ubuntu-based machines by default, have internet access, and can install packages. Cursor says the GitHub integration can clone a repository and push work to a separate branch. The newer Cloud Agent documentation says runs can produce artifacts that help a reviewer validate what happened.

For a team, this means the useful evidence is not simply the agent's final sentence saying that the task is complete. Look at the actual diff. Check the tests the agent ran. Check whether it changed files outside the requested scope. Look for dependency changes, configuration edits, migrations, permission changes, and generated files.

Cursor also documents Agent Review, which can review local changes automatically after an agent task or manually from the source-control interface. That can add a second automated pass, but it should complement rather than replace the team's normal review process. See https://prod.cursor.com/docs/agent/agent-review.

Why It Happens

AI coding agents are optimized to produce a useful implementation from incomplete natural-language instructions. That means they must make assumptions. An agent may choose a library, change an abstraction, add a dependency, modify tests, or interpret an ambiguous requirement in a way that compiles but is not what the team wanted.

Remote execution adds another layer. The agent can access a repository, install packages, and use network-connected tools. Cursor's documentation explicitly discusses repository permissions, secrets, network access, and security considerations for background agents. That makes environment boundaries part of the engineering design, not an afterthought.

The right question is therefore not "Can the agent write the code?" It clearly can. The better question is "What evidence do I require before this code becomes part of the product?" That answer should be defined by the repository's normal merge process.

Working Fixes

Start with a precise task description. State the files or subsystem the agent should touch, the behavior that must remain unchanged, the tests that must pass, and the definition of done. If a task involves sensitive logic, say so explicitly.

Next, require the agent to explain its implementation in terms of the repository rather than generic best practices. Ask what it changed, why it changed it, what it tested, and what it could not verify.

When the pull request arrives, review in this order:

1. Read the requirement again before reading the implementation.
2. Inspect the file list. Unexpected files are an immediate reason to investigate.
3. Read the diff, not only the summary.
4. Check tests and add tests for edge cases the agent did not cover.
5. Review dependency and configuration changes separately.
6. Check authentication, authorization, file access, shell commands, network requests, and data handling.
7. Run the project's normal CI and local test suite.
8. Compare the implementation with existing repository conventions.
9. Ask the agent to explain or revise anything you cannot confidently defend.
10. Merge only after the human reviewer understands the behavior.

GitHub's own pull-request guidance treats review as a place to inspect changes, comment, suggest edits, and decide whether a change is ready to merge. See https://docs.github.com/en/pull-requests/get-started/reviewing-pull-requests-quickstart.

A useful additional check is to ask the agent to list every assumption it made. This often exposes hidden decisions about error handling, defaults, API behavior, or backwards compatibility. For larger changes, compare the final diff against the original issue line by line rather than relying on a generic implementation-complete statement.

What Doesn't Work

The weakest workflow is to accept "tests passed" as the only evidence. Passing tests do not prove that the feature matches the product requirement, that the security model is correct, or that the agent did not make an unrelated architectural change.

Another weak pattern is reviewing only the final summary. Summaries are useful navigation aids, but the diff is the source of truth for what changed.

It is also risky to give an agent broad repository access when the task only requires a small area. Cursor documents that background agents can receive repository access, secrets, and network access depending on configuration. The principle is simple: give an agent the access needed for the job, and keep the merge decision separate from the agent that produced the code.

Avoid another common trap: asking the agent to review its own work and treating that review as independent. A second automated pass can find useful issues, but it can share assumptions with the original implementation. Independent tests, repository checks, and human review provide different evidence.

Official Response

Cursor's documentation describes background agents as asynchronous remote agents that can edit and run code in an isolated environment. It also documents GitHub integration, web and mobile handoff, pull-request workflows, and Agent Review. These features are explicitly built around the idea that agent work can be inspected and handed off rather than silently merged.

The official documentation does not say that generated code is automatically safe or correct. Its security section instead highlights risks around repository permissions, secrets, network access, and automated command execution. That supports a review-first workflow: use the automation for speed, but preserve repository controls and human accountability.

Key Takeaways

Cursor's background agents are useful because they move implementation work into an asynchronous workflow. The important engineering change is not that developers no longer need to code; it is that the boundary between writing code and reviewing code becomes more important.

For small tasks, a lightweight diff review and focused test run may be enough. For large refactors or security-sensitive changes, use deeper review, broader tests, and explicit checks of dependencies and configuration. Cursor's Agent Review can help surface problems, while GitHub pull requests provide the collaboration and approval layer.

The safest default is simple: agent proposes, tests provide evidence, human reviews, repository controls decide what merges.

Related Reading

For teams using AI coding agents, the most useful companion documentation is Cursor's background-agent documentation, its Agent Review documentation, and GitHub's pull-request review guidance. These sources explain the mechanics of remote agent execution and the review steps that remain important before code is merged.