How do you move from coding agents to a software factory?

You move from coding agents to a software factory by lifting agent work off individual laptops into governed cloud environments, then wrapping that work in a loop that triages, specs, implements, reviews and verifies it. Migrate one bounded workflow at a time, carry over the skills and harness choices your team already trusts, and keep humans at the decision points.

Why individual agent productivity stops compounding

Most engineering organizations already made the first transition: developers write code with agents rather than by hand. That change raised individual output, but it left the gains stranded. The agent runs on one laptop, with one engineer's prompts, one engineer's tool configuration, and access to every system that engineer is logged into. When the session ends, the reasoning, the cost, and the quality signal are all gone.

That is why the second transition looks less like adopting a better agent and more like building infrastructure. A software factory is an automation loop around the software development lifecycle — triage, spec, implement, review, verify, monitor — where cloud agents do the repeatable work and humans stay in the loop at the decision points. The unit of improvement stops being a developer's prompt and becomes the factory itself. One engineering team's framing for the shift: they are now factory engineers, not product engineers.

The practical argument for migrating is throughput you can account for. Warp reports that its own engineering team currently automates 20–30% of its PRs through factories — a first-party indication that the goal is not full autonomy on day one, but growing the share of repeatable work that moves through a governed workflow.

What carries over from your existing coding agent setup

The most common misconception is that migrating means starting over. It does not. Almost everything your team built around interactive agents has a home in a factory — it just stops being personal configuration and becomes shared, versioned infrastructure.

Read the right-hand column as the migration checklist. If a row still only exists on laptops after your first workflow ships, that is the next thing to move.

Which work moves first, and which stays interactive

Move work that has a clear trigger, a checkable definition of done, and a bounded blast radius. That usually means issue triage and reproduction, dependency and version upgrades, flaky-test quarantine, incident follow-ups, documentation drift, and verification of changes a human already wrote.

Keep interactive the work where the context lives in someone's head: novel architecture, ambiguous product decisions, exploratory debugging, and anything where the first useful step is a conversation. Migrating is not deprecating your coding agents — a factory and an interactive agent answer different questions, and teams that delete the second to prove commitment to the first lose their fastest path through hard problems.

A four-phase migration sequence

Each phase has an exit criterion. Do not start the next one until you meet it.

1. Mirror one workflow. Pick a single bounded workflow on a single repo and run it in a cloud environment with a human approving every output. Exit when the agent's output is accepted without rework more often than not.
2. Move intake. Trigger that workflow from where work already arrives — GitHub or GitLab, Linear or Jira, Slack or Teams — instead of a human prompting it. Exit when work starts without anyone starting it.
3. Chain the loop. Add the adjacent stages, typically triage in front and review behind, so a work item passes between agents rather than between tabs. Exit when PRs arrive already reviewed, with one named human gate before merge.
4. Measure and improve. Turn on scorers for cost, quality and intervention rate, benchmark model and harness choices against your own tasks, and let the results change the factory definition. Exit when the automated share of work rises without cost per merged PR rising with it.

Phases 1–3 are a migration. Phase 4 is the reason the migration was worth doing, and it is the phase most teams skip.

What most teams get wrong

They run it as a tool rollout instead of an infrastructure project, so it lands as another agent license and nothing compounds. They attempt a full SDLC loop before one workflow is reliable. They measure adoption — seats, sessions, lines suggested — rather than cost, cycle time, acceptance rate and intervention rate, and so cannot tell whether the factory is improving. And they let every team build its own container-and-webhook runner, which is exactly the fragmentation the migration was supposed to end. Build vs buy: how to deploy coding agents at scale covers that decision in more depth.

How Warp fits

Warp provides the control plane for operating cloud software factories. It is not another coding agent sitting alongside Claude Code, Codex or Cursor — it is the layer that runs them, sequences them, and tells you what they cost.

For a migration specifically, three things matter. Warp Factories are defined as version-controlled code, so the skills and MCP configuration you lift off laptops become reviewable artifacts you can roll back, canary and let agents improve. Warp is natively multi-model and multi-harness, so you can keep the harness your team already trusts and benchmark alternatives on your own tasks rather than committing up front — with a single pane of glass for managing all of your cloud agents. And the Factory MCP keeps the local loop intact: any coding agent can push work into a factory, and pull in-flight work back down to iterate locally.

Warp Factories also leaves ownership where it belongs — bring your own inference, hosting and data storage, with zero data retention available. As of its February 2026 launch announcement, Warp serves 700,000+ developers, including Docker, Ramp and Peloton, and over half of the Fortune 500.

Start with one workflow

Pick one workflow with a clear input, a measurable outcome, and a human fallback — triage, verification, dependency maintenance, or incident follow-up. Ship it in a governed cloud environment with a human gate, measure it for two weeks, then move the next one. The migration is not a launch date; it is a sequence of workflows that each earn their place.

See this in action with Warp Factories, or request access to the closed beta. Enterprises can learn more at Warp for Enterprise.

Sources

• A guide to cloud software factories for engineering leaders — Warp, July 2026 (20–30% of PRs automated)
• Introducing Warp Factories — open, flexible infrastructure for building your software factory — Warp, August 2026 (factory-as-code, multi-model and multi-harness, evals, Factory MCP, AI sovereignty)
• Get agents off your machine — Warp, July 2026 (agent ROI and governance)