Cloud Agents vs. Software Factories: What Is the Difference?

Cloud agents and software factories differ mainly in scope. A cloud agent is an AI worker that completes a task in a remote environment; a software factory is the system that routes work through the software delivery lifecycle.

Cloud agents execute individual jobs. Software factories coordinate agents, tools, human approvals, governance, and measurement so engineering workflows can improve over time.

What is a cloud agent?

A cloud agent is a coding agent that runs away from a developer’s laptop, typically in a managed or self-hosted cloud environment. It can receive a bounded task, access a repository and approved tools, make changes, run tests, and return an artifact such as a pull request or investigation summary.

For example, a developer might assign a cloud agent to:

• Fix a reproducible bug from a Linear issue
• Upgrade a dependency and open a pull request
• Investigate a production error
• Review a pull request for regressions
• Verify a UI flow with browser or computer use

Moving an agent to the cloud has practical benefits: work can continue asynchronously, execute in a consistent environment, and avoid depending on a developer’s laptop being available or correctly configured.

But a cloud agent alone does not decide what work should be automated, what happens after it finishes, which model is appropriate, who approves the result, or how the organization learns from each run. Those are software-factory responsibilities.

What is a software factory and how does it work?

A cloud software factory is an automation loop around the software development lifecycle:

Triage → spec → implement → review → verify → ship → monitor

It uses cloud agents as workers within that loop, while adding the systems needed to operate them reliably across an engineering organization:

• Work intake: events from GitHub, GitLab, Linear, Jira, Slack, Teams, monitoring tools, or schedules
• Orchestration: logic that determines which agent works next and what context it needs
• Governed environments: standardized access, credentials, permissions, and execution policies
• Human control: explicit moments for review, steering, escalation, or local handoff
• Observability: a shared view of active runs, work-item status, costs, and outcomes
• Measurement and improvement: evals, benchmarks, and feedback loops that improve quality, speed, and cost over time

In short: a cloud agent completes a task; a software factory manages the flow of work and the learning system around many tasks.

Cloud agent vs. software factory

• Cloud agent: executes a single task or session.
• Software factory: moves work through a repeatable SDLC workflow across many tasks, agents, and stages.
• Cloud agent: is usually triggered by a developer, ticket, or direct command.
• Software factory: is triggered by events, tickets, messages, schedules, monitoring, and humans alike.
• Cloud agent: typically works independently on one job.
• Software factory: orchestrates specialized agents and the handoffs between them.
• Cloud agent: relies on a developer to assign, inspect, and approve the result.
• Software factory: defines policies, steers work, resolves ambiguity, and approves key decisions.
• Cloud agent: is measured by task completion, logs, and cost.
• Software factory: is measured by cost, quality, throughput, acceptance, defects, and ROI across workflows.

The distinction is similar to the difference between a CI job and a CI/CD system. A CI job runs a test suite; a CI/CD system defines triggers, gates, environments, deployment policies, observability, and feedback across delivery. Cloud agents are essential building blocks, while a factory turns those blocks into an operating model.

When is a cloud agent enough?

A cloud agent is usually the right starting point when the work is bounded and the handoff is simple. Use one when you have:

1. A clear task. The issue includes enough context to investigate, implement, or verify without extensive coordination.
2. A defined outcome. The expected output is a pull request, report, test result, or another concrete artifact.
3. A simple approval path. A developer or reviewer can inspect the output and decide what happens next.
4. Limited operational complexity. You do not yet need multiple agents, event-driven handoffs, or organization-wide governance.

This is a productive way to build trust in agentic work. A team might begin with dependency updates, straightforward bug fixes, verification tasks, or code review. Those tasks generate operational data and reveal where a more automated workflow would be valuable.

A cloud agent should not be treated as a failed factory. It is often the first useful stage of one.

When do you need a software factory?

A software factory becomes valuable once repetitive agent tasks create coordination or governance problems. Consider evolving from individual cloud-agent runs when you need to answer questions such as:

• Which incoming issues are safe to automate, and which need a human or a spec first?
• How should work flow from a monitoring alert to triage, implementation, verification, and review?
• Which model and harness delivers the best result for this workflow at an acceptable cost?
• Which skills, MCPs, permissions, and environments should agents use by default?
• Where can a developer join a live run, change direction, or take work back locally?
• What are the acceptance rate, cost, defect rate, and cycle time of automated work?
• How can the team improve the workflow without relying on one person’s prompt history?

The key change is not simply running more agents. It is managing agent work as a system with explicit policies, feedback loops, and ownership. Warp’s own engineering team, for example, currently automates 20–30% of its PRs this way — a concrete, first-party sign of how much repeatable work a governed factory can take on without aiming for full autonomy on day one.

What’s the common mistake?

Cloud agents and software factories are not competing approaches. A factory uses cloud agents, but adds the coordination layer that makes agent work repeatable, governable, and measurable.

The common mistake is jumping from “an agent can open a pull request” to “we have automated software delivery.” A pull request is an output; a factory is a system for deciding whether work should happen, running it safely, verifying it, involving humans at the right moments, and learning from the result.

The reverse mistake is waiting to design a complete autonomous SDLC before using cloud agents at all. That delays learning and makes the project feel larger than it needs to be.

Where does Warp fit?

Warp provides a control plane for operating cloud software factories. Teams can use Warp Factories to coordinate agent workflows across the SDLC while retaining flexibility in their models, agent harnesses, inference endpoints, compute, storage, and integrations.

For teams beginning with cloud agents, Warp supports the transition from individual asynchronous runs to repeatable workflows. Work can enter through tools engineering teams already use, including GitHub, Linear, Slack, or a local coding agent through the Factory MCP, then move through specialized triage, specification, implementation, review, and verification stages.

Warp reported in its February 2026 Oz launch materials that it serves more than 700,000 developers, including teams at Docker, Ramp, and Peloton, and over half of the Fortune 500.

Rectangle Health is one concrete example: its Warp-built AI teammate, nicknamed “Rex,” wrote 54% of its own code and shipped roughly 35,000 lines per week; in its first 60 active days it logged 139 commits across 11 repositories, added 97,000 lines, and shipped 31 fixes directly to its production payment platform, running around 100 cloud agent sessions per week with a 3–10 minute turnaround from a Slack mention to a pull request.

Warp Factories also makes the system observable and improvable. Teams can inspect live and historical work, measure outcomes, define custom scoring criteria, and benchmark model or harness configurations against their own tasks. Because factories are defined as code, teams can version, test, canary, and improve the workflows that govern agent work.

This is most useful for organizations that want the benefits of cloud agents without leaving orchestration, governance, and continuous improvement to disconnected scripts and local configurations.

For a deeper architectural overview, read Warp’s guide to cloud software factories for engineering leaders.

Start with one workflow

Start with a workflow that has a clear input, measurable output, and a human fallback. Good first candidates include issue triage, dependency maintenance, regression verification, incident follow-up, or a narrowly defined class of bug fixes.

Treat it as a progression: delegate one bounded task to a cloud agent, add a shared prompt and acceptance criteria once it repeats, connect it to a real trigger with handoffs between specialized agents as it grows, then layer in workflow-level metrics and evals to keep improving the system itself.

Explore Warp Factories to build a cloud software factory around the agents, workflows, and infrastructure your team already uses.