Cloud coding agents

A cloud coding agent is an AI coding agent that runs outside your laptop in a remote development workspace. It can clone a repository, inspect code, install dependencies, run commands, make changes, run tests, and prepare a pull request when code changes are needed.

The important distinction is where the work happens. Local assistants help while a developer is actively editing code. Cloud coding agents can keep working after a task is delegated because the runtime, repository checkout, terminal, files, and environment live in managed compute.

1. Delegate work

   Start from a GitHub issue, Linear ticket, Slack request, or dashboard prompt.

2. Run in the cloud

   Replicas creates an isolated workspace with repository context and development tools.

3. Review the output

   Inspect the session output, file changes, test results, or pull request when the task is done.

Teams use cloud coding agents when they want coding work to continue away from an individual developer machine, but still remain visible and reviewable.

Async by default

Assign work and keep moving while the agent works in a remote development workspace.

Familiar agent harnesses

Use cloud workspaces with coding agents your team already uses locally, including Claude Code and Codex.

Isolated compute

Each task runs away from your laptop in a sandboxed environment built for code changes.

Team-visible work

The task, workspace, files, session output, test results, and pull request stay inspectable.

Workflow-native inputs

Start work from the places engineering teams already use: GitHub, Linear, Slack, and the dashboard.

Cloud coding agents work best when the task can be described, inspected, tested, and reviewed. Many tasks end as pull requests, but engineering work can also end as test results, reproduction notes, or a recommendation.

• Turn GitHub issues into pull requests
• Assign Linear tickets to coding agents
• Delegate bug fixes from Slack
• Implement small product changes
• Run extensive end-to-end tests
• Investigate flaky failures and summarize findings
• Follow up on code review comments
• Clean up engineering backlog items

The standard coding agent artifact is a pull request because implementation work needs code review. But the broader reason cloud coding agents work is that many software engineering tasks can be delegated to an isolated workspace.

Pull requests

The standard output for implementation work: changed files, commits, and a reviewable branch.

Test runs

Long-running verification work such as browser E2E tests, local reproduction steps, or release checks.

Investigation notes

A trace of what the agent inspected, commands it ran, failures it found, and recommended next steps.

Operational tasks

Repository maintenance, dependency checks, setup validation, or other engineering work that does not always need a code change.

The important question is not whether an agent can edit code once. It is whether your team can delegate work repeatedly, inspect the result, and keep control of access, execution, and review.

Trust

Can the platform run the coding agent harnesses your engineers already trust, instead of forcing every team onto a new agent experience?

Inference control

Can your team use existing provider subscriptions, API keys, credits, or enterprise agreements where the underlying agent supports them?

Infrastructure separation

Is the platform charging for the cloud workspace, orchestration, and collaboration layer separately from model inference?

Reviewability

Can people inspect the session, commands, test logs, file changes, and final output before trusting the result?

• Does it run tasks in isolated cloud workspaces?
• Does it support the agent harnesses engineers already use locally?
• Can teams bring their own inference path through supported subscriptions, API keys, credits, or enterprise agreements?
• Can it connect to GitHub and open reviewable pull requests when code changes are needed?
• Can work start from Linear, Slack, GitHub, or a dashboard?
• Can humans inspect session output, file changes, test logs, and final notes?
• Can teams configure environments, secrets, and repository access?

Cloud describes where the work runs: remote compute with a repository, development environment, terminal, files, and enough context to make progress away from an individual laptop.

Background describes the delegation model: the task can continue asynchronously after a developer assigns it. That does not mean humans disappear from the workflow. In Replicas, people can inspect sessions, review files, comment on changes, steer work, and decide what ships.

• Best cloud coding agents

  Compare cloud coding agent platforms by workflow fit, trust, cost, and orchestration model.

• AI coding agents

  Learn how AI coding agents differ from autocomplete, chat assistants, and local IDE tools.

• Background coding agents

  Understand asynchronous delegated coding work and how it relates to cloud workspaces.

• AI code review agent

  See how Replicas turns PR reviews, CI failures, and automated checks into follow-up work.

• Automated code review

  Learn how automated review findings can become action, not just comments.

• Workspaces docs

  Learn how Replicas workspaces run coding tasks in isolated development environments.

• GitHub integration

  Connect repositories and trigger Replicas from GitHub workflows.

• Linear integration

  Assign Linear issues to Replicas and turn planned work into pull requests.

• Slack integration

  Delegate engineering work from Slack conversations.