What is an AI software engineer? How they work and 8 options compared
Published October 2, 2026
Summary
An AI software engineer is a coding agent that takes a task from ticket to pull request: it reads the codebase, plans the change, writes and tests the code, and hands the result to a person for review. Compare eight products by which agent does the work, where it runs, and where tasks start.
Definition
What is an AI software engineer?
An AI software engineer is an AI agent that works on software tasks without a person steering every step. Given an issue, a bug report, or a failing build, it explores the repository, decides what to change, edits the code, runs the tests, and returns a pull request or an explanation. A person still reviews and merges the work.
The term became widespread in March 2024, when Cognition introduced Devin as an AI software engineer. Most coding tools have since added a mode that works this way, so the label now describes a way of working more than a single product.
- Autocomplete suggests the next lines while you type. You write the code.
- A chat assistant answers questions and drafts snippets. You apply and test them.
- An editor agent edits files and runs commands while you watch and steer it.
- An AI software engineer takes a delegated task, works on it without you watching, and hands back a result to review.
The loop
How an AI software engineer works on a task
Products package it differently, but every task goes through the same loop.
- Intake
- A task arrives from an issue tracker, a chat message, a pull request comment, a failed CI run, a schedule, or an API call.
- Environment
- The agent gets a machine with the repository, dependencies, services, and credentials it needs. Whether that is your laptop, a container, or a VM decides what the agent can run and what it can reach.
- Plan and implement
- The agent reads the relevant code, writes a plan, and edits files. Some products show the plan for approval before any code changes.
- Verify
- The agent runs tests, linters, and the application itself, and fixes what fails. Products that give the agent a browser can check UI changes visually.
- Hand off
- The agent opens a pull request or writes up what it found. Reviewers comment, and the agent can continue from that feedback.
Fit
What AI software engineers do well, and where they struggle
They are most useful on work that is well scoped and easy to verify. Results drop when the task is ambiguous or the environment cannot run the code.
- Good fits: bug fixes with a reproduction, review follow-ups, CI failures, dependency upgrades, small features from a clear ticket, test coverage, and dead-code cleanup.
- Harder: changes that need product judgment, decisions across teams, or context that lives in people's heads rather than in the repository.
- The environment matters as much as the model. An agent that cannot install dependencies, start services, or run the test suite cannot check its own work.
- Review becomes the bottleneck. Ten agents producing pull requests only help if someone can review ten pull requests.
Eight options
AI software engineers compared
All eight take a task and return reviewable changes. They differ in which agent does the work, where it runs, and where tasks can start. Product surfaces change quickly, so confirm details with each vendor.
| Product | Agent that does the work | Where it runs | Where tasks start | Best for |
|---|---|---|---|---|
| Replicas | Claude Code, Codex, Cursor, OpenCode, and other supported agents | One Linux VM per task; dedicated or self-hosted for enterprise | Slack, Linear, GitHub, GitLab, schedules, CI failures, API, dashboard | Teams that want delegated work across the agents they already use |
| Devin | Devin | Managed Devin cloud sessions; VPC deployment for enterprise | Devin app, Slack, Linear, GitHub | Teams that want one packaged autonomous engineer |
| Factory | Factory Droid with selectable models | Local machines or managed Droid Computers | CLI, desktop, web, Slack, Microsoft Teams, Jira, Linear, CI | Organizations standardizing on one agent across the SDLC |
| OpenAI Codex | Codex | Local CLI and IDE, plus OpenAI-hosted cloud tasks | ChatGPT, IDE, CLI, GitHub, GitLab, Slack, Linear | Teams standardized on OpenAI and ChatGPT plans |
| Claude Code | Claude Code | Local terminal and IDE, plus Anthropic-hosted cloud sessions | Terminal, web, and routines on schedules, API calls, or GitHub events | Teams standardized on Anthropic |
| Cursor | Cursor Agent with a curated model selection | Local editor; Cloud Agents in isolated VMs | IDE, web, mobile, Slack, GitHub, Linear, API | Developers who want one editor-to-cloud workflow |
| GitHub Copilot cloud agent | Copilot | Ephemeral GitHub Actions environments | GitHub issues and pull requests | Organizations that live entirely in GitHub |
| OpenHands | OpenHands agent, model-agnostic | Local, OpenHands Cloud, or self-hosted | GUI, CLI, SDK, Slack, Jira, Git providers | Teams that want an open-source agent they can host |
Replicas: best for delegating work to the agents your team already uses
Replicas is a cloud agent platform for delegated engineering work. A task can start from Slack, Linear, GitHub, GitLab, a schedule, a failed CI run, or the API, and each one runs in its own Linux VM with the repository, dependencies, services, and integrations prepared before the agent starts.
Instead of one built-in agent, teams choose Claude Code, Codex, Cursor, OpenCode, or another supported agent per task, using existing model credentials where supported. Reviewers can watch the session, take over the desktop or browser, comment on the diff, and have the agent continue in the same workspace.
Devin: best for one packaged autonomous engineer
Cognition's Devin popularized the category. It takes a scoped task through planning, implementation, testing, and a pull request with its own agent and workspace, and takes work from Slack, Linear, and GitHub. The trade-off is that every task runs through Devin rather than an agent your engineers choose.
Factory: best for standardizing on one agent across the SDLC
Factory's Droids cover coding, review, testing, and reliability work and run from the command line, desktop, web, chat, issue trackers, and CI. Models are configurable, but every task executes as a Droid, so it suits organizations adopting one agent system top-down.
OpenAI Codex: best for teams on ChatGPT plans
Codex runs locally and as cloud tasks in OpenAI-managed containers, started from ChatGPT, the IDE, the CLI, or mentions in GitHub, GitLab, Slack, and Linear. Cloud tasks draw from the ChatGPT plan and run Codex only.
Claude Code: best for teams on Anthropic
Claude Code runs in the terminal and IDE, and Anthropic also hosts cloud sessions that run in parallel and return pull requests for GitHub repositories. Routines, in research preview, start cloud work from schedules, API calls, or GitHub events.
Cursor: best for an editor-to-cloud workflow
Cursor pairs its editor with Cloud Agents that run in isolated VMs, can test the software, and return pull requests. Cloud work runs through Cursor Agent rather than external agents such as Claude Code or Codex.
GitHub Copilot cloud agent: best for GitHub-only organizations
Assign a GitHub issue to Copilot and it works in a GitHub Actions environment, then opens a pull request. It only works with repositories hosted on GitHub, and each session has a time limit.
OpenHands: best for an open-source agent you can host
OpenHands is open source and model-agnostic. Run it locally, use OpenHands Cloud, or self-host it in your own VPC on the Enterprise plan. Self-hosting puts environment management and scaling on your team.
Decision guide
How to choose an AI software engineer
Start with which agent you want doing the work. If your engineers already trust Claude Code or Codex, a provider-native cloud product or a platform that runs those agents keeps that trust. If you want one vendor to own the agent and the workflow, Devin and Factory are built for that.
Then look at the environment. The agent needs what a new engineer needs on day one: the repository, dependencies, services, test data, and credentials. Ask how each product prepares that environment, how long it takes to start, and whether the agent can run the application and a browser.
Next, check where tasks start and where results go. A product that only takes tasks from one place only covers the work that starts there, so map it against the issue tracker, chat, source control, CI, and schedules your team uses.
Finally, test on your own backlog. Run the same five tasks through each finalist: a review follow-up, a CI failure, a small feature from a ticket, a flaky test, and a cleanup pass. Compare setup effort, how often you had to step in, the evidence returned, and total cost.
FAQ
AI software engineer questions
Getting started with Replicas
Delegate a real task and review what comes back
Replicas includes a 14-day free trial with no credit card required. Connect a repository, pick Claude Code, Codex, Cursor, or OpenCode, and assign a task from the dashboard, Slack, Linear, or GitHub. The agent works in its own cloud workspace and returns a pull request for your team to review.