Cursor Background Agents: how to review code when the AI worked while you were away

2026-04-29 · 5 min read · ZenCode

Cursor Background Agents let you queue autonomous coding tasks that run in isolated remote cloud VMs while you are offline, in a meeting, or working on something else. Unlike Cursor Composer, where the agent edits your local working copy in real time and you watch it happen, Background Agents work asynchronously: you describe the task, the agent runs it in a fresh VM, and you return later to a completed diff waiting for your review. The shift from synchronous to asynchronous changes not just the workflow but the psychology of review.

The code the agent produces is ordinary code — the same quality you would get from Composer or Cursor’s inline AI on a comparable task. What changes is the frame you are in when you first see it. Background Agents remove the three friction points that normally keep reviewers alert: you did not watch the agent make decisions, the diff appears complete rather than incremental, and “it ran successfully in the VM” sounds like a test result. Each of these produces a distinct review trap.

The three Cursor Background Agent attention traps

1. Temporal gap trust

When you watch an in-IDE agent work in real time, its decision points are visible as they happen. You see it choose a data structure, pick an API method, or decide where to add error handling. Even if you do not intervene, you absorb enough context to hold the agent’s reasoning in mind when the diff appears. That ambient awareness makes the final review feel continuous with what you already observed.

With Background Agents, that continuity is severed. You return to a completed diff after an hour, a day, or a weekend. The agent made every decision without you present, and you are now being asked to review the result with no memory of the reasoning. The psychological default in this state is to treat the diff the way you treat an email you forgot to answer: you skim it, form a quick impression, and move on. The temporal gap creates a rubber-stamp reflex that does not occur when review is synchronous.

The fix is to treat a Background Agent diff the same way you treat a PR from a contractor you hired for a short task: with no assumption that you know what decisions were made, requiring explicit justification for every non-obvious choice, and with the same skepticism you would apply if the contributor were unfamiliar. The agent’s absence from your review session is not evidence that it worked correctly — it is a reason to be more careful, not less.

2. Parallelization diffusion

Background Agents are designed to run in parallel. You can queue multiple tasks simultaneously, each running in its own VM, each producing a separate diff. The intended benefit is throughput: tasks that would serialize into hours can finish in parallel within one focused session. The review trap is that parallel diffs that look clean individually can conflict silently at the level of shared system behavior.

Agent A rewrites how your application resolves configuration values. Agent B adds a new feature that depends on configuration resolution. Each diff is internally consistent. The feature diff references the right config keys, the schema is valid, the types match. But Agent B was written against the old resolution logic — the one Agent A replaced. The feature will fail at runtime in a way that neither diff reveals, because the conflict is between assumptions made in two separate VMs that never shared state.

This is qualitatively different from merge conflicts, which tools can detect. These are semantic conflicts: each change is internally valid, the conflict only becomes visible when you reason about how they interact. The parallelization that makes Background Agents productive also removes the sequential review that would catch this class of error. The fix is to review parallel diffs as a set rather than independently: before accepting any individual diff, identify every shared system component that two or more agents touched, and verify that each agent’s assumptions about that component still hold after the other agents’ changes are applied.

3. Remote isolation false safety

Background Agents run in ephemeral cloud VMs that are isolated from your production environment. The VM has no access to your real database, your production secrets, your actual load, or the system state that your application has accumulated over time. When the agent completes successfully — tests pass, the build finishes, no errors are reported — it is easy to read that as evidence the code is safe for production.

It is not. The VM’s isolation is precisely what makes it possible for the agent to finish cleanly. A migration that truncates a column passes in a VM with a fresh test database. An authentication change passes in a VM with seeded test credentials. A background job passes in a VM with no competing workload and no accumulated queue depth. “Worked in the cloud VM” means the code is syntactically and structurally plausible under controlled conditions — it does not mean it behaves correctly in your actual environment.

This trap is sharper than it looks because the VM output genuinely resembles a real test result. It is not a hallucination or a plausible-sounding assertion — the tests actually ran and passed. The problem is that the test environment does not model the things most likely to cause production failures: real data shape, real auth state, real infrastructure constraints, and real runtime load. Treat VM pass/fail as a necessary condition, not a sufficient one. Before merging any Background Agent diff, identify what environmental assumptions the VM was making that your production environment does not share, and verify those assumptions hold separately.

What good Background Agent review looks like

Background Agents change when review happens, not whether it happens. Because the agent works asynchronously, the review workload is concentrated at the end rather than distributed across the session. That concentration is a feature only if you treat it as a deliberate review gate — a point at which you apply the same skepticism you would to a PR from an external contributor with no prior context on your system.

Concretely: block time for review before you accept the diff, not while you are context-switching back into the work. Read the full diff linearly, not just the files you expected the agent to touch. Treat every non-obvious implementation choice as a decision that needs a rationale you can verify. Identify shared dependencies across parallel diffs and check them as a unit. And test the changes in an environment that reflects your actual production state, not just the VM conditions the agent ran in.

The Background Agent model is genuinely useful for the tasks it is designed for: self-contained changes with clear scope, stable interfaces, and good test coverage. The traps above emerge most sharply when agents are queued on tasks with hidden dependencies, when parallel tasks share system components, and when the VM test environment is substantially simpler than production. Knowing which tasks fit the model is as important as knowing how to review the results when they do not.

Related: Cursor Composer review · Cursor AI IDE review · Cursor BugBot review · GitHub Copilot agent mode review · Devin AI autonomous agent review · Goose by Block review · Best AI coding tools 2026