An AI software factory and a CI/CD pipeline answer different questions. CI/CD automates building, testing, and releasing a change once it exists, while a factory covers a wider loop: where the change comes from, the environment it is built in, how it is validated as a whole system, and how it reaches production. A factory generally uses CI/CD as part of its deploy step rather than replacing it.

What CI/CD does well

CI/CD runs a defined sequence on every commit, compiling the code, running tests, building an artifact, and releasing it through stages. It is deterministic and well understood, and it assumes the change already exists and mostly needs verifying and shipping, which holds for human teams merging a manageable number of branches.

Where CI/CD strains once agents are involved

Two of those assumptions weaken when agents produce the changes. Changes no longer arrive one branch at a time from people; agents produce many at once and need isolated environments with the infrastructure a change touches, which a standard pipeline does not create. Verification is no longer mostly automated tests either, since agent output often needs a person to confirm behavior in a running system, which a pass or fail on a test suite does not provide. CI/CD was built to verify human changes, and giving ten agents somewhere to work and a way to earn trust falls outside what it was designed for. The effect compounds, and DORA's 2025 research found that every 25% increase in AI adoption came with a 7.2% drop in delivery stability, and that a high-quality platform amplifies the effects of AI adoption, since AI amplifies whatever system it is dropped into.

What a factory adds

A factory adds the stages around the pipeline: task intake, an isolated environment per change with the infrastructure it needs, and validation in a running environment before merge, plus the control plane that sets the policy deployments run under. A platform like Encore provides these stages, creating a per-change environment with the database and queues the change needs and deriving the deploy from the code, while your existing pipeline still runs the release. The deploy step itself can stay a CI/CD pipeline, now fed changes that were built and validated in running environments rather than raw branches.

Side-by-side

	CI/CD	AI software factory
Scope	Build, test, release an existing change	Intake to production, including where the change comes from
Assumes	Human-authored changes, one branch at a time	Many agents working in parallel
Environments	Shared staging or ephemeral, infrastructure usually set up by hand	Isolated per change, with the infrastructure created automatically
Verification	Automated tests	Running environment plus tests, behavior confirmed by a person
Deployment	The core job	One stage, deterministic from code

Do you still need CI/CD

Yes. A factory does not remove the need to build, test, and release deterministically; it wraps that work in the stages agents require, and in practice the pipeline becomes one component of the factory's deploy stage, fed better-validated inputs.

For the concept, see what is an AI software factory. To build one on top of what you have, see how to build an AI software factory.