What Is Trunk-Based Development?

Quick answer
Trunk-based development keeps one shared branch, called trunk or main, and merges small changes into it at least daily.
Branches are allowed but short-lived: hours or a couple of days, not weeks. Long-running feature branches are the practice it replaces.
Incomplete work ships behind a feature flag or a disabled route rather than living on a branch.
It requires a fast, trustworthy CI pipeline, because every merge to trunk must be safe to release.
It reduces merge conflicts, which matters more when several AI agents or assistants edit the same repository.

What is trunk-based development?

Trunk-based development is a branching model in which developers integrate into a single shared branch frequently — at least once a day, and ideally on every completed small change. Branch lifetime is measured in hours, not sprints. The shared branch is always expected to be releasable, which is why the CI pipeline runs on every merge.

The alternative, often called Git Flow, keeps feature branches alive for days or weeks and merges them at the end. That model optimises for isolating unfinished work and pays for it with integration risk: the longer a branch lives, the more its merge becomes a second, untested change.

How is it different from Git Flow?

Git Flow uses long-lived develop, release, and hotfix branches, and merges between them. Trunk-based development removes that hierarchy: one trunk, short-lived topic branches, and releases cut from trunk. The coordination that Git Flow performs through branches is performed through flags, tests, and deploy automation instead.

| Attribute              | Trunk-based                    | Git Flow                              |
|------------------------|--------------------------------|---------------------------------------|
| Long-lived branches    | One trunk                      | main, develop, release, hotfix        |
| Branch lifetime        | Hours to 2 days                | Days to weeks                         |
| Integration frequency  | At least daily                 | At merge time                         |
| Incomplete work        | Behind a flag                  | On a branch                           |
| Merge conflict risk    | Low                            | Rises with branch age                 |
| Prerequisite           | Fast CI, flags, deploy automation | Discipline about branch hygiene    |

What has to be true for it to work?

Three things. The pipeline must run quickly enough that waiting for green is not a context switch, and reliably enough that a red build means a real problem. Features must be able to ship dark, which means a flag or a route that is not linked yet. And changes must be small enough to review, because the safety of merging to trunk depends on each merge being understandable.

Without those, trunk-based development degrades into committing directly to main and hoping. The practice is not the absence of branches; it is the presence of verification on every integration.

Why does it help teams using AI coding agents?

Because agents multiply parallel edits. Two assistants working on separate long-lived branches will produce a merge that neither of them reviewed as a whole, and the conflict resolution is often done by a third assistant that has less context than either. Short-lived branches bound how much divergence can accumulate.

Small merges also make review tractable. A generated change of two hundred lines can be read; four generated branches merged at once cannot. Pair trunk-based development with a rule that an agent’s branch lives for one task, and the integration cost stays proportional to the task rather than to the week.

Where this bites vibecoders
An AI assistant asked to build a feature will often scaffold it on a new branch, then keep adding to that branch across several sessions because it is the path of least resistance. A week later the branch cannot merge cleanly, and the resolution is generated by the same assistant that wrote both sides. The cheaper habit is to merge the first small piece behind a flag before starting the second.

Where AI coding assistants get this wrong

Letting an agent accumulate a week of work on one branch because the branch is also its memory.
Merging directly to trunk with no test run, then calling it trunk-based development.
Shipping incomplete UI without a flag, so trunk is technically broken for users.
Resolving a large merge conflict with an assistant that never saw either original change.
Using long-lived environment branches (staging, prod) as the integration point.
Reviewing a two-thousand-line generated diff as if it were a small change.

Checklist

Merge to trunk at least once per working day.
Keep topic branches to one task and under two days.
Put incomplete user-facing work behind a flag.
Require a green pipeline before every merge.
Keep pull requests small enough to review honestly.
Delete branches after merge instead of reusing them.

FAQ

Does trunk-based development mean no branches at all?

No. Short-lived topic branches are normal, and many teams use them for every change. The defining rule is that branches are merged within a day or two, so trunk remains the place where integration actually happens. Branches that live for weeks defeat the model.

Is trunk-based development safe for a small team?

Yes, and it is often easier for a small team because there are fewer people to coordinate. The requirements are a test suite that runs on every merge and a way to ship incomplete work dark. A solo developer already has the second condition naturally.

How do you handle code review with daily merges?

Review the small change before it merges, using a pull request that lives for hours. The review is faster because the diff is small, and the merge is faster because the branch has not drifted. Daily merging makes review easier, not optional.

What about release branches for hotfixes?

A short-lived release branch cut from trunk for a specific version is compatible with trunk-based development, provided the fix is also merged back to trunk. Long-lived release branches that receive features are not; they recreate the divergence the model is meant to avoid.