An AI software factory raises throughput, and it also changes what the engineers on the team spend their time doing. With agents writing most of the code and building it against working infrastructure, the effort that used to go into typing and wiring services moves to deciding what to build and confirming it was built well. A backend team's week starts to look different from the one it had before.

Specifying replaces typing

An engineer's input becomes a well-scoped task at the start and a judgment at the end, so writing a clear ticket, with the goal and the constraints an agent needs, matters more than it used to, because a vague task produces a vague change no matter how capable the model is. Once the agent has worked, the engineer looks at the result rather than the process, and judging whether the change is right matters more than having written it. Engineers who were already good at deciding what to build and at spotting when something is subtly wrong do well in this setup.

The backlog runs in parallel

Each agent gets an environment of its own, so a team can put many tasks in flight at once instead of moving them through one or two people one at a time. The backlog turns into a queue that agents pull from, so the constraint moves from how fast engineers can type to how fast the team can specify work and review results. The gain shows up as many tasks progressing at once rather than any single one finishing sooner, which is a different shape of speed than a faster individual developer.

Review happens by running, not reading

Review changes the most, because reading a large diff someone else wrote to decide whether it works is slow and error-prone, and it gets worse when the author is a model producing more code than a person would. AI-authored pull requests already wait 4.6 times longer for a first review (LinearB), and a factory replaces the reading with a running environment attached to each change, where a reviewer exercises the feature, checks traces when something is off, and merges on the evidence of having seen it work.

Without it, the extra throughput just piles into a review queue, and a team that generates ten times the pull requests but still reviews them by reading has moved the bottleneck rather than removed it, which is the failure mode most teams hit first.

The platform team sets rules, not approvals

The platform team's role moves from approving changes to setting the rules changes run under. Permissions, the resources agents may create, and the deployment policy are defined once, in a control plane separate from the loop agents work in, and agents then move inside those limits without a platform review per change. The team adjusts the boundaries in one place when something needs to change. DORA's 2025 research found that a high-quality platform amplifies the effects of AI adoption, since AI amplifies whatever system it is dropped into, which is why this role becomes more important as agent volume grows, not less.

What stays with the people

The team's judgment stays central even as the mechanical middle moves to the factory, since someone still decides what is worth building, confirms a finished change is right, and owns the parts of the system where a mistake is expensive, while the wiring between services, the environments, and the repetitive review of straightforward changes are handled for them. The work that carries the most judgment stays with the engineers, and the factory takes the mechanical middle so their time goes where it matters most.

Encore is one platform built for this workflow, handling the environments, validation, and deploys the model needs, though the shift in how a team works comes from the model itself rather than any one tool.

For what a factory is and the parts it needs, see what is an AI software factory, and for building one, see how to build an AI software factory.