What Is a Software Factory, and How Does It Compare to Agentic Loops?
A software factory is a cloud-based system that automates the full software development lifecycle — triage, spec, implement, review, verify, ship, monitor — across every task a team ships, with humans approving key decisions. An agentic loop is the plan-act-observe cycle one agent runs to finish a single task. A factory chains many agentic loops together and adds the orchestration, memory, and governance a lone loop lacks.
What an agentic loop actually is
An agentic loop is the reasoning cycle inside one agent: it reads context, plans a step, takes an action — writes code, runs a command, calls a tool — observes the result, and repeats until the task is done or it hits a stop condition. Every interactive coding agent, from Claude Code to Codex to Cursor, runs some version of this loop; it's what makes an agent an agent rather than a single-shot completion.
The loop is scoped to one task and usually one session. When the session ends, so does its working memory. Nothing about the loop itself tracks whether the task should have gone through review, whether it duplicates other work in flight, or whether the agent should have had access to production credentials in the first place.
What a software factory adds on top of the loop
A factory takes that same loop and wraps it in a pipeline: triage decides what work needs to happen, an implementation agent runs its own loop to write the code, a review agent runs a separate loop to check the work, and a verification step — often computer use — confirms the fix actually works before it ships. Each stage is its own agentic loop; the factory is the layer deciding which loop runs next, what context it inherits from the last one, and where a human needs to sign off.
That's the structural difference: an agentic loop optimizes one agent finishing one task well. A factory optimizes a stream of tasks moving through a full lifecycle — reliably, with governance, and in a way that gets measurably better over time.
Agentic loop vs. software factory
| Agentic loop | Software factory | |
|---|---|---|
| Scope | One agent, one task, one session | Many agents, many tasks, full SDLC |
| Memory | Lives and dies with the session | Persists across runs; feeds self-improvement |
| Orchestration | None needed — one loop, one actor | Routes work between triage, spec, implement, review, verify |
| Governance | Whatever the operator sets up ad hoc | Built-in human checkpoints and audit trail |
| Improvement | Better prompting or context, per session | Evals and scorers tuned across every run |
Where teams get this comparison wrong
The common mistake is assuming that chaining a couple of coding agents — one for implementation, one for review — already makes a factory. It doesn't, if there's no shared memory between runs, no consistent triage step deciding what enters the pipeline, and no measurement of whether the whole thing gets cheaper or better over time. That's still a collection of agentic loops, just more of them. A factory implies the pipeline is defined, versioned, and improving as a system. For the layers that separate a real factory from a stack of loops, see Warp's guide on what tools make up a modern software factory; for the related question of one cloud agent versus the system around it, see cloud agents vs. software factories.
How Warp fits
Warp Factories is the control plane for chaining agentic loops into a governed pipeline — not another agent running its own loop alongside Claude Code or Codex. Its foreman agent takes a work item, decides which agent picks it up next (triage, spec, implement, review), and passes context forward so each loop isn't starting cold. Warp reports that its own engineering team automates 20–30% of its PRs through factories built this way — a first-party sign that the value comes from chaining loops into a measured pipeline, not from any single loop getting smarter. Because factories are defined as code, teams can version the pipeline, swap models or harnesses per stage, and let observer agents propose improvements to the definition itself. For the full picture of how a governed loop works end to end, see Warp's guide to cloud software factories.
Start with one workflow
Don't try to convert every agentic loop your team runs into a full factory on day one. Pick one workflow with a clear input, a measurable outcome, and an obvious human fallback — triage of incoming bug reports is a common starting point — and wrap just that loop in triage, review, and measurement. Expand into spec and verification once that stage is reliable. You can get started with Warp Factories at warp.dev
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