
Scope with the business and lock the plan.
Sprint the build; adjust as reality lands.
Test it, train them, and set it up in their accounts.
Held in reserve — then review, present and grade.
3+ unit-style assertions · ONE validated binary judge · a one-page error-analysis memo. Binary pass/fail, never 1-to-5 — range scores are nearly impossible to align with human preference. (The non-AI majority of your build proves itself the class-09 way: tests green, checklist rows demonstrated — this gate covers the parts that can be wrong in new ways.)
An LLM judge is a meta-eval — it needs to be evaluated. Measure its true-pass and true-fail rates against held-out human labels before trusting it. (Class 14 gave you the loop; this block runs it on the partner build.)
You can’t fully specify quality until you grade real outputs — and grading changes the criteria (UIST 2024). So: write criteria → grade 20 real outputs → document what changed. That memo is part of the deliverable.
Deliverable + written criteria + deadline → a 5–10-business-day review window → written acceptance, with a cure period for anything short. The calendar makes the order strict: criteria walkthrough at visit 3, the window closes before the visit-4 install, and anything short gets cured in the remote stretch — documented, the fix demoed over video.
Development is 20–40% of a tool’s lifetime cost; maintenance is 60–80% — and three-quarters of that is enhancement, not bug-fixing. The part agents accelerated was the small part.
83.2% of maintenance commits on AI-generated files are still performed by humans (2026 study — 1,016 files across 100 repos). The orphaned-app death of the 4GL era runs on the same mechanism: nobody on staff understands the code well enough to touch it.
A $10K-equivalent custom tool loses roughly 3:1 to a $200/month SaaS over three years — unless it replaces three or more subscriptions, kills per-seat pricing, or does what no vendor sells. Write that arithmetic into the handoff doc, with this tool’s actual numbers.
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