An AI software factory is a pipeline of coding agents that takes a written spec and turns it into a tested, reviewed pull request in your repository. Think of an assembly line: one station plans, one writes code, one runs the tests, one checks the work. People still decide what gets built and what gets merged. This guide walks through each station, shows which tools already cover it as of 3 November 2026, and explains where things tend to break.
The idea is not new. Teams have long used the word "factory" for build systems and CI pipelines. What changed is that the middle of the line, the part where someone actually writes the code, can now be handed to an agent. That shifts the hard work to both ends: writing a clear spec at the start and reviewing the result at the end.
The short version
What an AI Software Factory Actually Does
A normal coding assistant waits for you to type, then suggests code. A software factory works from a queue. A ticket lands in Linear, Jira or GitHub Issues. An agent picks it up, works in its own isolated copy of the repo, runs the checks and opens a pull request. You get a notification when there is something to review.
Two things make this possible. First, agents now run in the cloud, in their own sandbox, so they can work for hours without your laptop. Second, they can run your test suite and read the results, so they can fix their own mistakes before a human ever looks.
The Pipeline, Station by Station
Every factory we have seen follows roughly the same order. The names change from vendor to vendor, but the stations do not.
| Stage | What happens | Who owns it | Typical output |
|---|---|---|---|
| 1. Spec | Someone describes the change, the reason, and how to tell it is done | Human | Ticket or spec file with acceptance criteria |
| 2. Plan | The agent reads the code and proposes the files and steps it will touch | Agent, human can approve | Short plan or task list |
| 3. Code | The agent edits files in an isolated branch or sandbox | Agent | Commits on a branch |
| 4. Tests | The agent runs the test suite, linters and type checks, then fixes failures | Agent + CI | Green CI run, new tests |
| 5. Review | A reviewer agent and then a person read the diff | Agent first, human last | Review comments, fix-up commits |
| 6. Pull request | The change is proposed with evidence: logs, screenshots, test results | Agent opens, human merges | Mergeable PR |
Spec: the station that decides everything
An agent can only build what it is told. A vague ticket like "make the checkout faster" gives it room to guess. A good spec says what to change, why, which files are off limits, and how to check the result. Cognition's own Devin documentation puts it plainly: clear prompts with explicit completion criteria raise the success rate.
Plan and code: where agents are strongest
Planning and writing code are what current agents do best. They read the repo, follow its conventions and make changes across many files. Most tools let you keep project rules in a file the agent reads on every run, such as CLAUDE.md for Claude Code.
Tests and review: the quality gate
Tests are what turn a guess into a claim. An agent that runs the suite, sees a failure and fixes it is far more useful than one that only writes code. Review then happens twice: an automated reviewer catches the obvious issues, and a person checks intent, design and risk.
Which Pieces Exist Today
You do not have to build every station yourself. Here is what the main tools document, based on their own pages as of 3 November 2026.
- OpenAI Codex (cloud): runs coding tasks in cloud environments, each with its own workspace, and keeps working while your computer sleeps. You inspect the changed files, ask for follow-up changes, then commit or open a pull request. See the Codex cloud docs.
- Claude Code GitHub Actions: you mention @claude in an issue or PR, and Claude implements the change and pushes commits. It can also run a review skill on every pull request. The Claude Code GitHub Actions guide says it can turn issues into pull requests and advises you to review Claude's changes before merging.
- Cursor Cloud Agents: each agent runs on its own virtual machine, tests its changes and can open a pull request with videos, screenshots and logs attached. You can start one from the editor, the web, or by mentioning @Cursor in Slack, GitHub or Linear. Details are in Cursor's background agents help page.
- Devin: Cognition's agent works on Linear or Jira tickets, writes tests, checks that CI passes and returns draft PRs for review.
- OpenAI Symphony: not a coding agent but an orchestration spec. It watches a Linear board, starts isolated agent runs and asks for proof of work such as CI status, review feedback and walkthrough videos. The openai/symphony repository calls itself a low-key engineering preview for trusted environments, licensed under Apache 2.0. We covered it in detail in our OpenAI Symphony guide.
Notice the pattern. The coding agents cover stations 2 to 4 and open the PR. Symphony-style orchestrators cover the conveyor belt between the ticket queue and the agents. Nobody ships the spec or the merge decision for you, and that is on purpose.
Where Humans Stay in the Loop
A software factory does not remove people. It moves them to three points where judgment matters most.
- 1The spec. A person decides what to build and writes the acceptance criteria. This is product work, not typing.
- 2The review. A person reads the diff with the evidence the agent attached. Reviewing machine-written code is harder than it looks, as we explain in why reviewing LLM code is hard.
- 3The merge. A person presses the button. Branch protection rules should make sure no agent can merge to main on its own.
Common Failure Modes
Most problems in a software factory are not about the model being "not smart enough". They come from the setup around it.
- Vague specs. The agent fills the gaps with guesses. The PR looks fine and solves the wrong problem.
- Weak tests. If the suite is thin, a green run means little. An agent can even adjust tests until they pass. Review test changes as carefully as code changes.
- Review fatigue. Ten agents can open more PRs than two people can read. Volume goes up and review quality goes down unless you limit how many tasks run at once.
- Too many permissions. The agent only needs access to the repo and the tools for the task. Secrets, production databases and deploy keys should stay out of its sandbox.
- Runaway cost. Every run burns compute and tokens. The Claude Code docs suggest capping turns and setting workflow timeouts; similar limits exist in most tools.
- Lost context on long tasks. A task that spans many hours can crash halfway. Durable execution, covered in our guide to durable AI agents, lets the agent resume instead of starting over.
How to Start Small
You do not need the full line on day one. A safe way to begin:
- 1Pick one repo with decent test coverage.
- 2Write down your rules in the agent's instruction file: style, forbidden folders, how to run tests.
- 3Start with small, well-defined tickets: bug fixes, dependency updates, missing tests.
- 4Require a green CI run and one human approval on every agent PR.
- 5Track how many agent PRs get merged without major rework. Grow only when that number is steady.
How Halmob Helps
Halmob builds software factories: agent pipelines that turn a spec into reviewed, tested pull requests in your repo. We set up the spec template, the agent sandbox, the test and review gates, and the branch rules that keep merges in human hands. For work that runs for hours, we add durable agents that keep their state and resume after failures.
If you are still choosing between Codex, Claude Code, Cursor or other platforms, our agent platform consultancy helps you pick and roll out the right one. To talk about your repo and your team, get in touch.