No single product today is a complete AI software factory; a factory is assembled from a handful of layers, and most tools cover one or two of them well. Choosing between options comes down to working out which layer each tool covers and where it leaves you to fill the rest, from the coding agent that writes a change through the environment it runs in and the infrastructure available there to validation and the path to production.

What to evaluate

A tool's contribution to a factory comes down to a few questions:

• Does it give each agent an isolated environment, so agents can run in parallel without colliding?
• Does that environment contain the database and queues a change needs to be tested against, or only mocks?
• Can a change be validated end to end before a person trusts it?
• Is deployment to your cloud deterministic and governed, or improvised by the agent?
• Can you adopt it on your existing codebase and cloud accounts without a rewrite?

Few tools answer all of these, so a factory is usually a combination of them.

Coding agents

Cursor's background agents, Devin, Claude Code, and OpenAI's Codex sit here. They produce the code, differ in quality and interface, and on their own stop at a diff or a pull request. A coding agent is one component of a factory, since it does not provide isolation, infrastructure to test against, or a deployment path, so teams pick an agent they like and build the rest of the factory around it.

Sandbox and environment platforms

e2b, Daytona, Modal, and Vercel Sandbox give agents somewhere isolated to run, providing ephemeral compute you can place an agent in, some of it fast to start. They cover isolation, but the database and queues a change needs still have to be stood up inside the sandbox yourself, which is why they are not a factory on their own.

Deployment platforms

This layer covers what sandboxes leave out: creating the application's infrastructure inside each environment, validating changes against it, and deploying to your cloud. SST and Nitric take an infrastructure-from-code approach here, and a Terraform or Pulumi setup wired to a CD pipeline reaches the same result with more to assemble by hand. Encore also sits here, declaring resources through an infrastructure SDK so it can create them per environment for agents and provision them deterministically at deploy time from static analysis, within guardrails a platform team sets. Most teams underestimate this layer, since it is the hardest to build and the part a coding agent and a bare sandbox do not give you. See AI software factory architecture for how it fits together.

CI/CD and internal developer platforms

Existing CI/CD and IDP tooling can carry parts of the deploy and policy work, and a factory does not replace them. They were not built for per-change environments with the infrastructure a change needs and validation at agent speed, so they sit alongside a factory rather than being one, which AI software factory vs CI/CD goes into.

How to choose

Where to start depends on what you already run. With no coding agent in use, begin there, since it is the most visible gain and the easiest to trial. If you already run agents and struggle to trust or ship their output, put the effort into the infrastructure and validation layer, the part that turns a diff into a change you can deploy. Most teams combine a coding agent they like with a platform that handles environments, validation, and deployment.

For the concept these tools serve, see what is an AI software factory. To assemble one yourself, see how to build an AI software factory.