An implementation pipeline takes a change from a plan all the way to finished, reviewed code — automatically. This article walks through the stages a deep implementation pipeline runs, so you know what's happening while it works. Your own pipelines can include more or fewer stages; this is the full shape.
1. Plan or task input
A pipeline can start two ways: from a plan you've already made (Plan-ready — it skips straight to analysis), or from a task you hand it (Task-ready — it writes the plan first).
2. Complexity analysis
The pipeline sizes up the work. Complexity determines how much it does downstream — how many review cycles it runs, how many reviewers it pulls in, and its iteration limits. Bigger changes get more scrutiny; small ones don't pay for review they don't need.
3. Plan refinement
Multiple models refine the plan together, playing to their different strengths — one may be stronger at clear communication and structure, another at catching edge cases. They go back and forth, improving the plan until neither is suggesting more changes.
4. Partitioning and concurrent implementation
This is the core of a good implementation pipeline. When there's both frontend and backend work, the plan is split into three parts: a transport layer and the frontend and backend that depend on it. The transport is built first; then the frontend and backend are implemented concurrently, each handled by the model best suited to it. Splitting the work this way plays to each model's strengths and lets the independent pieces run in parallel once their shared contract is in place.
5. QA loop
The pipeline verifies its own work and fixes what it finds, looping until things pass. A QA loop can include several kinds of runs — compliance checks, rebuild checks, bug hunting, and security reviews. This is usually where a deep pipeline spends most of its time.
6. Summarize and ship
When the work is done, the pipeline summarizes what it did. If you've set it up to, it can push the branch and open a pull request for you.
How long it takes
A deep implementation pipeline can run for a long time — from minutes to hours, depending on the size of the feature, with most of that time in the QA loop. Because it runs in the background, the usual pattern is to plan a few things, fire off their pipelines, and move on to other work while they run. See Working in parallel.
Next steps
- Reading a pipeline execution — follow the run and act on the result.
- Build a verification framework — a QA loop is only as good as the tests it can run.
- Pipelines: overview — run modes and where pipelines live.