AI coding agents have dramatically shortened the distance between an idea and working software. They can inspect a project, create files, run commands, write tests, and help diagnose failures.
What they have not eliminated is judgment.
A polished screen is not proof that data survives a reload. A passing unit test is not proof that keyboard users can complete the core task. A successful deployment is not proof that the intended commit reached production.
This is why I use proof gates: observable conditions that must be satisfied before a product claim becomes stronger.
A proof gate is not a meeting, a long document, or an excuse to slow down. It is a compact question:
What evidence would let another person verify that this claim is true?
Here are five gates that separate a persuasive AI demo from a small MVP you can responsibly ship.
AI makes feature generation cheap, which makes uncontrolled scope especially dangerous.
Before requesting code, define one primary user in one specific situation. Then describe:
This becomes the product’s core loop.
For example, “build a productivity platform” is too broad. A more testable loop might be:
The gate is not passed because a form exists. It is passed when a new user can complete the entire loop and explain what changed without coaching.
Write a Not Today list alongside the required capabilities. Authentication, dashboards, collaboration, billing, and AI-generated summaries may all be reasonable later. They should not compete with proof of the first useful loop.
The goal is not the fewest possible features. It is the smallest complete behavior that tests whether the product creates value.
Many demos treat persistence as an implementation detail. For a real user, persistence is part of the product promise.
Define the data contract before allowing generated code to spread storage assumptions across the application. For every persisted field, specify:
Persisted data should carry enough version information to be interpreted safely. A backup envelope might include:
product
formatVersion
schemaVersion
exportedAt
records
The application should also distinguish between states that are often incorrectly collapsed into “empty”:
A dangerous pattern is catching a parse error, returning an empty array, and later saving that empty state over the user’s last valid data. Recovery behavior must be designed, not improvised inside an error handler.
The strongest practical test is a real round trip:
A download button proves that a file can be created. The round trip proves that recovery works.
“Build the rest of the app” gives an agent too much freedom and gives the reviewer too little evidence.
A bounded implementation prompt should contain five parts:
Goal:
Boundaries:
Acceptance criteria:
Verification:
Report:
The goal names one user-visible outcome. Boundaries state what must not change. Acceptance criteria describe observable pass-or-fail behavior. Verification lists the tests and commands that must run. The report requires exact results, changed files, remaining risks, and unverified behavior.
For bugs, separate diagnosis from editing. Provide expected behavior, actual behavior, reproduction steps, and the first relevant error. Ask the agent to trace the code path and identify the smallest experiment that would confirm the suspected cause.
Only then request the focused fix and its regression test.
This reduces opportunistic refactoring and makes failures easier to understand. It also creates a clean review question: did this change satisfy its stated contract without changing unrelated behavior?
Fast code is useful. Reviewable code is shippable.
A green test suite can still coexist with a broken product if the tests assert implementation details instead of user-visible behavior.
Test the domain rules, but also test the complete actions users depend on:
Then check the interface beyond the happy path.
Keyboard order should follow the task. Focus should remain visible and return logically after dialogs. Errors should be associated with the relevant controls. Essential actions should remain usable at 320-pixel width and 200% zoom.
Automated accessibility tools are valuable, but they cannot prove the whole interaction. A manual keyboard path and narrow-layout check often expose problems that component tests miss.
Report results in three groups: passed, failed, and unverified. “No automated violations found” is not the same claim as “the workflow is accessible.”
A successful local build is evidence about the build, not the deployment.
Before release, reconcile documentation with implemented behavior. Run the complete relevant test suite, type check, configured lint checks, and production build. Inspect for secrets, debug output, dead placeholders, and unexplained console errors. Record the release commit and a known rollback target.
After deployment, verify the live product in a clean session:
If a high-impact live check fails, the honest outcome is a diagnosed release problem—not a successful launch announcement.
The “one-day MVP” frame can be useful, but it should operate as a scope clock. It is not permission to skip data safety, recovery, accessibility, or release verification.
For each gate, record:
This record does not need to be elaborate. Its purpose is to stop confidence from becoming detached from evidence.
AI can help us build much faster. The corresponding professional skill is learning when the available proof justifies saying, “This is ready.”
If you want the complete workflow, From Idea to Verified MVP is a 32-page evidence-first guide with a separate 7-page copyable companion pack of briefs, contracts, prompt templates, schedules, and release checks: Get the bundle here.