# AI Coding Agents

AI coding agents are software engineering tools that can read a codebase, plan work, edit files, run commands, verify changes, and return reviewable output.

- Canonical: https://tryreplicas.com/ai-coding-agents
- Start a workspace: https://tryreplicas.com/auth?mode=signup
- Cloud coding agents: https://tryreplicas.com/cloud-coding-agents

## What is an AI coding agent?

An AI coding agent is different from autocomplete. It can take a goal, inspect the codebase, use tools, make changes, run checks, and iterate toward an output.

The output may be a pull request, but it can also be an explanation, a test report, a reproduction path, or a set of recommended next steps.

## The main types of AI coding agents

The market is converging around a few different surfaces. They are not interchangeable, and the best choice depends on how your engineers want to work.

- **IDE-first agents:** Best when the developer is actively pairing in an editor and wants fast local control over context and changes.
- **CLI and terminal agents:** Best for engineers who want a familiar command-line harness that can read files, edit code, and run commands.
- **Cloud coding agents:** Best when teams want to delegate tasks to remote workspaces that continue running away from a laptop.
- **Workflow agents:** Best when work starts from issues, PR comments, CI failures, Slack threads, or recurring automation triggers.

## What AI coding agents are good at

Agents perform best when the task has real repository context and a verification path. The goal is to turn software work into something the agent can execute, test, and hand back.

- Implement a scoped product change.
- Fix a bug with reproduction notes.
- Run or repair failing tests.
- Respond to code review comments.
- Investigate flaky CI and summarize findings.
- Update docs, scripts, migrations, or dependency setup.

## Where AI coding agents still need judgment

Coding agents are strongest when they get clear goals and feedback. They are weaker when the real task is product ambiguity, architectural tradeoffs, stakeholder alignment, or access to missing production context.

The best teams treat agents as execution capacity with review, not as a replacement for taste, prioritization, or ownership.

- Give agents verifiable goals, tests, and acceptance criteria.
- Review code and reasoning before merging.
- Use branch isolation and least-privilege credentials.
- Prefer small delegated tasks before broad open-ended rewrites.

## Where Replicas fits in the AI coding agent stack

Replicas is not trying to replace every coding agent harness. It is the cloud workspace and workflow layer for teams that want to run trusted agents against real engineering tasks.

That makes Replicas especially useful for teams that already adopted Claude Code, Codex, Cursor, or Opencode and want the same agent workflows to run asynchronously in the cloud.

- **Trust:** Engineers can keep using agent harnesses they already know instead of evaluating a completely new coding environment for every task.
- **Cost control:** Teams can separate workspace infrastructure from model inference and use existing provider subscriptions, credits, keys, or agreements where supported.
- **Workflow fit:** Tasks can start from GitHub, Linear, Slack, automations, or the dashboard instead of only from an active editor session.
- **Reviewability:** Work remains inspectable through sessions, files, commands, test logs, final notes, and pull requests when code changes are needed.

## How to choose an AI coding agent

Start with workflow fit, then evaluate quality. A great editor agent and a great cloud agent can both be valuable, but they solve different coordination problems.

- Is the primary workflow pairing, delegation, automation, or review follow-up?
- Does the agent run where your code and tools can be safely accessed?
- Can it verify changes with the same commands your engineers use?
- Can your team control model access, subscriptions, credits, and credentials?
- Does the result fit your review process?

## FAQ

### Are AI coding agents different from AI coding assistants?
Yes. Assistants usually help with suggestions or chat. Agents can use tools, edit files, run commands, and work through multi-step software tasks.

### Do AI coding agents always produce pull requests?
No. Pull requests are common for implementation work, but agents can also return test results, debugging notes, investigation summaries, or recommendations.

### Should teams use one coding agent or several?
Many teams use several because the surfaces are different. An IDE agent can be best for active pairing, while a cloud agent platform can be best for delegated background work.

## Related docs

- [Cloud coding agents](https://tryreplicas.com/cloud-coding-agents): See how AI coding agents run in managed cloud workspaces.
- [Background coding agents](https://tryreplicas.com/background-coding-agents): Learn how agents continue delegated work asynchronously.
- [Best cloud coding agents](https://tryreplicas.com/best-cloud-coding-agents): Compare cloud agent platforms and choose by workflow fit.