PDCA Harness turns each AI-driven contribution into one Plan → Do → Check → Act cycle. A deterministic driver does the work; a human stays at the three touch points that matter — authoring the spec, signing off the check, and improving the process.
PDCA — Plan, Do, Check, Act: the improvement loop from quality control, applied one contribution at a time.
What it is
An AI model can write a plausible fix for almost any issue in seconds — which is exactly the danger. A plausible diff is not a verified one, and at volume the maintainer becomes both the bottleneck and the only safety net. PDCA Harness funnels AI-generated contributions through a quality cycle so each one arrives scoped, proven, and ready to sign off.
Render the Copier template into a new project, concretize it to your tracker / branches / gates once, and a deterministic driver advances every contribution through a file-derived state machine — stopping only where a human is irreducible.
How it works
Why it holds up
The one thing allowed to block a sign-off is a deterministic, LLM-free check: a test that is red before the fix and green after it. Everything a gate can't adjudicate — an ambiguous-scope lint, an environmental failure, the irreducibly human judgments — surfaces as an explicit checklist for a person, never silently passed or silently blocked.
State lives in files, so the driver is idempotent and resumable; one planning session can brief several issues that build unattended and queue for a fast, cheap-first sign-off. And a full cycle rehearses entirely offline — stub models, stub gates — so you can learn it and prove the plumbing before spending a token.
See it for real
The walkthrough drives one real contribution — a fix to the Gramps genealogy project — through every beat: the actual brief, the actual patch, the gate table, the §6 checklist, a two-iteration sign-off, and the Act entry that turned a maintainer's review comment into a new gate. Plan → Do → Check → Act, on a live codebase.