# What is a software factory? 5 products to evaluate in 2026 > Source: > Published: 2026-08-17 00:00:00+00:00 The term “software factory” is becoming popular enough to become vague. Sometimes it means an AI coding assistant. Sometimes it means a fleet of autonomous agents. Sometimes it is used to describe the entire path from an issue in Linear to a tested pull request and, eventually, production. I use the narrowest definition that is still useful: **a software factory is a repeatable system that converts engineering intent into verified software changes.** The agent is part of that system, but it is not the whole system. A model can write a plausible patch. A factory has to provide the right environment, run the build, enforce the checks, handle failure, preserve state, control access, and return evidence that the change works. That distinction matters when comparing products. Devin, Cursor, Linear, Factory, and Islo all participate in this category, but they package different parts of the factory. ## Software factory products compared These products are not ordered by an overall winner because they build software factories around different operating models. Devin, Cursor, Linear, Factory, and Islo all combine agents with execution and workflow, but they center on different parts of the factory. | Product | Best for | Core model | Execution environment | Main trade-off | |---|---|---|---|---| | [Cursor Cloud Agents](https://cursor.com/workflows/autonomous-agents)[Linear Coding Sessions](https://linear.app/coding-sessions)[Factory](https://factory.ai/product/software-factory)[Islo](https://docs.islo.dev/overview)This list focuses on products that can support delegated, asynchronous software work, rather than every tool with an agent mode. The environment distinction still needs precision. Devin and Cursor say each cloud agent runs in its own VM, Linear uses managed development sandboxes, and Factory offers persistent Droid Computers, so it would be inaccurate to say those products provide no isolation. The bigger differences are how configurable the environment is, how explicit the workflow can be, and whether teams can mix harnesses with deterministic stages. ## What makes a system a software factory? A coding agent becomes part of a software factory when the surrounding system can repeatedly handle five jobs. ### 1. Accept intent Work has to enter from somewhere: an issue, an incident, a pull request comment, a scheduled maintenance task, or a direct instruction from an engineer. The factory needs enough context to tell the difference between a task that is ready to run and one that still contains a product decision. A normal backlog is full of assumptions humans resolve without noticing. Agents either need those assumptions made explicit or a reliable way to escalate. ### 2. Give each agent a real, isolated environment The repository alone is not a development environment. An agent may also need the correct compiler, private packages, Docker, databases, browser tooling, internal documentation, test data, and access to selected engineering services. It needs those capabilities without inheriting every credential and file on a developer’s laptop. This is where many apparent model failures are actually infrastructure failures. A better prompt cannot install a missing private dependency or make a stripped-down container behave like a complex developer workstation. ### 3. Execute and coordinate the work The system has to plan, edit, test, wait, retry, and sometimes split a project across multiple agents. When tasks depend on one another, the factory needs more than a queue. It needs to know what is ready, what is blocked, and which branch or artifact another task should use. Long-running work also needs durable state. CI may take twenty minutes, a reviewer may respond tomorrow, and a machine may restart in between. The workflow should resume from known state instead of asking an engineer to reconstruct the session. ### 4. Verify the result Plausible is not correct. A useful factory attaches evidence to its output: tests run, checks passed, screenshots captured, paths exercised, and limitations the agent could not verify. Mandatory checks should live outside the prompt where the agent cannot quietly skip them. This is the difference between producing code and producing a reviewable change. ### 5. Govern delivery Autonomy needs boundaries. The system should know which actions are safe, which require approval, and which are prohibited. Editing a branch and running unit tests may be automatic. Merging to the default branch, changing infrastructure, reading production data, or deploying a database migration may require a human. Audit logs, scoped credentials, budgets, retry limits, and stop rules are part of the product, not administrative details added later. I have written more about the underlying architecture in [the four layers of software factories](/blog/four-layers-of-software-factories) and about the runtime primitives in [what is autonomous coding?](/blog/what-is-autonomous-coding). ## Devin: best for delegated engineering tasks [Devin](https://docs.devin.ai/get-started/devin-intro) presents the clearest delegation model: give an autonomous software engineer a task, let it work in its own environment, and review the result. Each Devin cloud session runs in an isolated VM with a terminal, editor, browser, and computer-use capabilities. It can write and test code, reproduce bugs, implement features, perform migrations, and return pull requests. The developer can watch, intervene, or leave the session running after closing their laptop. Devin can also [coordinate managed sessions in parallel](https://docs.devin.ai/work-with-devin/advanced-capabilities), create reusable playbooks from successful work, and schedule recurring sessions. That moves it beyond a single background agent toward a managed fleet. This makes Devin a strong fit for teams with a meaningful volume of bounded work: framework upgrades, test writing, repetitive fixes, migrations, internal tools, and tickets with objective completion criteria. The limitation is not that Devin lacks autonomy. It is that autonomy exposes weaknesses in the work definition. A ticket that depends on a conversation, an undocumented constraint, and one senior engineer’s memory is not ready for delegation simply because an agent can accept it. Devin will work best where the organization can provide context, executable tests, and a clear review bar. **Choose Devin when:** you want to hand off scoped engineering outcomes to cloud agents and manage several delegated sessions without designing the underlying agent runtime yourself. ## Cursor Cloud Agents: best for Cursor-centered engineering teams [Cursor Cloud Agents](https://cursor.com/workflows/autonomous-agents) extend Cursor from an AI-native editor into a system for background and parallel work. Each cloud agent runs on its own machine, can work for hours or days, and returns artifacts for review. Agents can be triggered directly, on a schedule, or through tools such as GitHub, Slack, and Linear. Cursor describes the model as directing fleets of agents: engineers define the task and review bar while agents plan, build, test, monitor, and maintain software in the background. The advantage is continuity. A team can use the same product for interactive codebase exploration, local implementation, pull-request review, and delegated cloud work. That reduces the distance between the agent developers already use and the factory they are gradually assembling around it. Cursor also supports multiple frontier models, rules, skills, plugins, and MCP integrations, so teams can encode repository guidance and connect the agent to internal tools. The trade-off is product gravity. Cursor can connect to many engineering systems, but its strongest workflow naturally centers on Cursor as the place developers direct and review agents. That may be exactly what an organization wants. A team seeking a harness-agnostic factory or more configurable orchestration may prefer a different product. **Choose Cursor Cloud Agents when:** Cursor is already the team’s primary development environment and you want the shortest path from interactive assistance to parallel, autonomous cloud work. ## Linear Coding Sessions: best for issue-to-code context [Linear Coding Sessions](https://linear.app/coding-sessions) close the gap between the place work is defined and the place an agent implements it. Assign an issue to Linear Agent—or start from a chat, comment, Slack thread, or Teams—and Linear gathers the surrounding issues, projects, documents, discussions, and customer requests. It then starts a coding session using Claude Code or Codex, prepares the repository in a managed cloud sandbox, writes the change, and returns a diff or pull request for review. The context is the reason Linear belongs in this list. A coding agent usually sees the prompt and the repository. Linear already holds the issue history, related work, product discussions, and affected customer context that explain why the change exists. Sessions are collaborative, so teammates can follow and steer the same work instead of passing an opaque agent run between them. Linear’s [managed development sandbox](https://linear.app/docs/coding-sessions) supports common Python, Ruby, Go, Rust, Java, and Node.js projects. It can install dependencies, start local applications, use browser automation, and capture screenshots or recordings as verification. Triage automations can also investigate incoming bugs and open coding sessions automatically. The trade-off is that the workflow remains issue- and prompt-centered. Linear owns excellent product context and collaboration, but the execution happens in its managed sandbox and through its supported session model. Teams that need persistent state across stages, a different harness, custom environment policy, or a workflow with several deterministic and agentic stages need a more configurable factory model. **Choose Linear Coding Sessions when:** the issue tracker is already the source of truth and you want the shortest path from shared product context to an implemented, reviewable change. ## Factory: best for broad SDLC automation [Factory](https://factory.ai/product/software-factory) uses the term Software Factory for its full platform, and it has the broadest lifecycle framing in this group. Its product maps automations across triage, code generation, validation, release, documentation, and monitoring. Teams can start with a single Droid, add scheduled automations for repeatable jobs, or use coordinated, multi-day missions for larger projects. The Factory application then exposes metrics, integration coverage, repository coverage, and the health of those automations. That breadth is useful, but it should not be confused with having the most capable coding agent or the most faithful development environment. Devin offers a more complete delegation experience, Cursor offers a stronger coding surface, Linear brings deeper product context, and Islo goes further on persistent, hardware-isolated development infrastructure. Factory’s differentiation is the lifecycle map and the orchestration around it. Factory also describes [model routing and several deployment options](https://factory.ai/news/software-factory), including bring-your-own-key and self-hosted configurations. Publicly, however, it provides less detail than Islo about whether those environments can reproduce a team’s full development stack, including Docker, stateful services, private dependencies, and customer-defined isolation boundaries. The result is a product to evaluate for recurring workflows across the SDLC, rather than for the deepest coding-agent experience or the most configurable development environment. **Choose Factory when:** lifecycle coverage and recurring automation matter more than having the deepest coding-agent experience or a harness-agnostic factory with configurable development environments. ## Islo: best for harness-agnostic, custom software factories [Islo](https://docs.islo.dev/overview) is built around persistent development environments and [multi-stage orchestration lines](https://islo.dev/software-factory/) that coordinate work across them. An Islo line is a workflow graph: agent or deterministic stages, conditional or agentic transitions, loops, and decision pauses when a run needs another attempt or human input. The harness is chosen per stage, so one line can use Claude Code, Cursor, Codex, or a custom agent without rebuilding the factory around one provider. Lines start manually, on a schedule, through webhooks, or from GitHub, Linear, and Slack. Teams use them for different jobs: scheduled red-team audits, QA agents that drive browser flows on a real stack, or an [implementation loop](/blog/how-we-stopped-working-for-our-agents) where a Linear issue moves through implementation, review, verification, and feedback. Stages run in persistent microVMs with Docker, stateful services, private dependencies, and gateway-managed secrets. Teams configure the image, resources, egress policy, and lifecycle rather than accepting a fixed sandbox. The trade-off is configurability. Teams define environments, stages, routing, and approval points instead of accepting a single-agent workflow. **Choose Islo when:** you need a harness-agnostic software factory with fully configurable development environments and policies, or multi-stage workflows with explicit routing, loops, and decisions. ## How to choose a software factory product Start with the workflow, not the vendor. Pick one repeated class of work and write down: - What triggers the task? - What context and tools does it require? - What evidence proves that it is complete? - Which actions can happen automatically? - Which actions require human approval? - When should the agent stop and escalate? Then choose the product whose operating model removes the most work without hiding the controls you still need. - Choose **Devin** for direct delegation of scoped tickets to autonomous cloud engineers. - Choose **Cursor Cloud Agents** when Cursor is already the center of development and you want its agents to continue in the background. - Choose **Linear Coding Sessions** when Linear already contains the product context and you want issues to flow directly into implementation and review. - Choose **Factory** when broad lifecycle automation matters more than depth in the coding agent or development environment. - Choose **Islo** when you need a fully configurable development environment and security policy, harness-agnostic execution, or a custom workflow with deterministic and agentic stages. Do not begin with the hardest feature in the backlog. Dependency updates, test creation, bounded migrations, lint fixes, and CI failure repair are better starting points because the inputs are visible and the result can be checked. The first useful measure is not lines of code or pull requests opened. Measure reviewed changes that merged without creating more work than they removed. ## Common questions **What is a software factory in software engineering?** A software factory is a repeatable system that converts engineering intent into verified software. It combines task intake, development environments, implementation, testing, delivery controls, observability, and human review. AI-native software factories use autonomous agents for some of the implementation and coordination work. **Is a software factory the same as an AI coding agent?** No. An AI coding agent can plan, write, and test code. A software factory is the larger system around one or more agents, including isolated compute, context, orchestration, verification, permissions, audit trails, delivery integrations, and human approval. **What are the top software factory products in 2026?** The five products engineering leaders should evaluate are Devin, Cursor Cloud Agents, Linear Coding Sessions, Factory, and Islo. Devin focuses on complete task delegation; Cursor extends an AI-native editor into cloud agent fleets; Linear connects product context directly to implementation; Factory provides broad SDLC automation with less depth in the coding and environment layers; and Islo combines harness-agnostic orchestration lines with persistent environments and custom workflows. **What are Linear Coding Sessions?** Linear Coding Sessions are managed agentic coding workflows inside Linear. Linear Agent gathers context from issues, projects, documents, discussions, and customer requests, then uses Claude Code or Codex in a cloud sandbox to investigate the task, write code, and return a diff or pull request for collaborative review. **What is the difference between Factory and Islo?** Both are software factory platforms. Factory.ai organizes Droids and automations across named SDLC stages. Islo orchestrates multi-stage workflows through its own line model, with conditional or agentic routing, loops, decision pauses, and a harness selected per agent stage, all running in persistent development environments on Islo’s managed cloud or an optional customer-cloud deployment. **Do Devin, Cursor, Linear, and Factory provide real isolated development environments?** They provide isolated execution, but not the same environment model. Devin and Cursor document a VM for each cloud agent, Linear uses managed development sandboxes, and Factory offers Droid Computers. Islo combines dedicated microVMs and persistent state with configurable images, resources, snapshots, lifecycle, services, egress rules, gateway-managed secrets, and a harness chosen per stage. The environment—including its policy and state—is a configurable, durable part of the software factory. **When should a team choose Islo?** Choose Islo when the development environment is too complicated for a temporary managed sandbox, you want to switch or combine agent harnesses, or the workflow requires explicit multi-stage logic beyond one prompt. Islo lines combine agent and deterministic stages with routing, loops, decision pauses, schedules, integration triggers, verification, and persistent state. **What does “beyond one prompt” mean in a software factory?** It means the workflow is defined as explicit stages rather than left entirely inside one agent session. Different stages can use different harnesses, run deterministic commands, share or isolate state, fan out in parallel, wait for external events, loop after failed verification, and pause for human decisions. The prompt supplies judgment inside a stage; the factory controls how the full process runs. **Can a software factory replace a development team?** No. Current software factories shift human work from producing every line toward defining tasks, encoding constraints, reviewing evidence, and handling decisions that require product or domain judgment. Weak requirements, missing tests, and undocumented systems remain engineering problems even when agents write the patch. **What should a team automate first?** Start with bounded, repeatable work that has objective checks and limited production risk, such as dependency updates, test writing, small migrations, lint fixes, documentation maintenance, or CI failure diagnosis. Avoid ambiguous product work and irreversible production actions until the environment, verification, and approval controls have proved reliable. **Does every software factory need multiple agents?** No. A useful software factory can begin with one agent and one repeatable workflow. Multiple agents become valuable when tasks can run independently or when specialized implementation and review agents add measurable value. Parallelism without task dependencies, isolation, and review capacity usually creates more conflicts rather than more throughput.