What Are Background Coding Agents? Running AI Agents While You Work
Background coding agents are AI agents that work on development tasks in the cloud while you do something else. How they work, when to use them, and how they differ from in-editor agents.
A background coding agent is an AI agent that works on a development task in a separate environment while you do something else. Instead of watching an agent work in your terminal, you describe the task, the agent runs in the background on remote infrastructure, and you review the result when it finishes.
The name comes from how they run: in the background, not in your foreground session. You can work on other things, take a meeting, or go home for the day. The agent keeps working.
What is the difference between background agents and in-editor agents?
In-editor agents work inside your coding session. Background agents work outside it.
| In-editor agent | Background agent | |
|---|---|---|
| Where it runs | Your machine, in your editor | Remote infrastructure |
| When you watch | While it works | When it finishes |
| Resource usage | Your CPU, your ports, your database | Separate environment |
| Parallelism | One at a time, typically | Many at once |
| Persistence | Stops when you stop | Keeps working |
An in-editor agent like Claude Code in agentic mode or Cursor agent mode works in your terminal or editor. You see it work, you can interrupt it, and it uses your machine's resources.
A background agent works somewhere else. You dispatch a task and move on. The agent runs in a cloud environment with its own resources. When it finishes, you get a pull request or a notification.
Why would I use a background coding agent?
Background agents let you parallelize work that used to be sequential. Instead of watching one agent work on one task, you can dispatch multiple agents to multiple tasks and review the results as they come in.
Common use cases:
- Bug fixes that do not need your supervision. A clear bug report with a reproduction can go straight to an agent.
- Mechanical refactors. Updating API calls, migrating syntax, renaming patterns across a codebase.
- Feature implementations from specs. When requirements are written down, an agent can work from them.
- Overnight or weekend work. Tasks can keep moving when you are not at the keyboard.
- Parallel exploration. Try three approaches at once and see which one works best.
The throughput gain comes from the agent working while you work on something else, not from the agent being faster.
How do background coding agents work?
A background agent runs in a cloud environment with your repository cloned and your development stack available. It reads the task, plans the work, writes code, tests it, and produces an artifact like a pull request.
The typical flow:
- Dispatch. You describe the task, either through a CLI, a web interface, or by linking to an issue.
- Environment setup. The platform provisions a cloud environment: a VM or container with your repository checked out and dependencies installed.
- Execution. The agent works through the task autonomously, running tests and verifying its changes.
- Artifact production. When done, the agent opens a pull request with the changes and evidence (screenshots, test output).
- Notification. You get notified that the PR is ready for review.
The agent does not need your attention during steps 2 through 4. That is what makes it a background process.
What makes a background agent reliable?
An agent that cannot verify its work is guessing. The difference between a useful background agent and a frustrating one is whether it can actually run the code it writes.
Reliable background agents have:
- A running stack. The agent can start services, execute tests, and interact with the application, not just write code.
- Self-verification. The agent tests its changes before opening a PR, catching its own mistakes.
- Evidence. The PR includes screenshots, test output, or recordings that show the change works.
- Post-PR automation. The agent responds to review comments and CI failures rather than handing them back to you.
An agent that only has a repository checkout and cannot run your stack will produce diffs that look plausible but fail at runtime.
What platforms support background coding agents?
Several platforms offer background agent capabilities with different tradeoffs.
- Murmur gives each agent a dedicated cloud VM with your full stack running. Agents handle review comments, CI failures, and merge conflicts. Supports bring-your-own-keys and running in your own VPC.
- Cursor cloud agents run agents in Cursor's cloud infrastructure, with recent updates allowing them to follow review comments and CI status.
- Devin runs autonomous agents in sandboxed cloud environments with high autonomy.
- GitHub Copilot coding agent runs agents through GitHub Actions, integrated with GitHub's workflow.
For a detailed comparison, see best cloud AI coding agents 2026.
How many background agents can I run at once?
With cloud infrastructure, the limit is budget and coordination, not machine resources. A laptop running local agents caps out at a few concurrent tasks. A cloud platform can run dozens.
The practical limit is often review capacity. If agents produce PRs faster than you can review them, the queue grows. This is why teams running agent fleets often adopt AI code review at the same time.
When should I use background agents versus in-editor agents?
Use in-editor agents for interactive work. Use background agents for autonomous work.
| Situation | Better choice |
|---|---|
| Exploring a problem together | In-editor |
| Quick fix you want to supervise | In-editor |
| Well-defined task with clear requirements | Background |
| Task that takes longer than you want to wait | Background |
| Multiple similar tasks in parallel | Background |
| Work that should continue overnight | Background |
The mental model is: if you would pair program on it, use an in-editor agent. If you would hand it to a teammate and come back later, use a background agent.
What happens when a background agent gets stuck?
Good platforms notify you and provide context. The agent might ask a clarifying question, report that it cannot complete the task, or surface what it tried.
Some platforms let you send follow-up instructions to a running agent. Others require you to cancel and restart with more context. The best platforms minimize this by giving agents enough context to work autonomously.
How do background agents handle code review comments?
Lifecycle-aware platforms keep the agent connected to what happens after the PR opens. When a reviewer leaves a comment, the agent reads it, makes the change, and pushes. When CI fails, the agent investigates and fixes.
Platforms without this hand post-PR work back to you. The agent opened the PR; now you relay every review comment, read every CI failure, and do every rebase.
Murmur handles this lifecycle automatically: agents respond to review comments, repair failing CI, rebase when branches diverge, and resolve merge conflicts.
Frequently Asked Questions
What is a background coding agent?
A background coding agent is an AI agent that works on a development task in a separate cloud environment while you do something else. You dispatch the task, the agent works autonomously, and you review the result when it finishes.
What is the difference between background agents and Copilot?
GitHub Copilot in its original form is an in-editor assistant that suggests code while you type. The GitHub Copilot coding agent is a separate feature that runs agents in the background through GitHub Actions. Background agents work autonomously on tasks; assistants work interactively with you.
Can background agents run tests?
Yes, if they have a proper environment. Background agents running in a VM or container with your stack can run your test suite, start services, and verify their changes. Agents with only a repository checkout cannot.
How long do background agents take?
It depends on the task. A small bug fix might take minutes. A feature implementation might take an hour. The point is that the time does not block you because the agent works in the background.
Do background agents work with private repositories?
Yes. Platforms that support background agents connect to your GitHub organization and can access private repositories you authorize. Security varies by platform, so check whether your code stays in your environment or goes to the vendor's cloud.
How do I get started with background agents?
Start with a clear, well-scoped task on a repository with good test coverage. Use a platform like Murmur, Cursor cloud agents, or the GitHub Copilot coding agent. Review the resulting PR like you would review a teammate's work.
What tasks are background agents best for?
Background agents excel at bug fixes with clear reproductions, feature implementations from written specs, mechanical refactors, and repetitive changes across many files. They struggle with product judgment and requirements that live outside the code.
Can background agents fix CI failures?
Yes, on platforms with lifecycle automation. When CI fails on a PR, the agent reads the failure, diagnoses the problem, and pushes a fix. Platforms without this require you to relay the failure and request a fix manually.
How do background agents handle merge conflicts?
On platforms like Murmur, agents rebase when the base branch moves and resolve merge conflicts automatically. On simpler platforms, merge conflicts require manual intervention.
Are background agents secure?
Security depends on the platform. Key questions: does the agent run in your VPC or the vendor's cloud? Who can access the credentials the agent uses? Are actions logged? Murmur supports running in your own GCP or AWS VPC with your own credentials.