Vibe Coding · QA

How to Use Acceptance Criteria Before Letting AI Modify a Game Mechanic

Reviewed by Brightory Editorial Team

Brightory creates practical AI guides and workflows based on research, testing, and documented AI usage patterns.

Define the mechanic as observable examples before implementation: starting state, action, expected state transition, edge cases, and behaviors that must not change. The agent should implement against those criteria and run them again after the edit.

4 min read · Updated Sep 7, 2026

Controller and laptop arranged for game prototyping. — AI Game Creation · field notes

Direct answer

Acceptance criteria convert intent into a contract

Vibe coding research consistently describes iterative generation and evaluation rather than reliable one-shot development. The strongest way to evaluate is to decide what success means before the model writes the patch.

Use examples, not slogans

“Make dash feel responsive” is a design goal. Acceptance criteria might include: dash begins within one frame of valid input, lasts 0.18 seconds, ignores repeated input until cooldown, preserves vertical velocity, and cannot trigger while stunned.

Include non-regression criteria

If changing dash, list movement behaviors that must remain unchanged. This prevents the agent from satisfying the new requirement by breaking an old one.

Keep taste-sensitive criteria separate

Numbers can create a baseline, but final feel still needs playtesting. Mark which criteria are hard pass/fail and which are human review so the agent does not pretend subjective design has been mathematically proven.

Working template

MECHANIC: [...]
START STATE: [...]
ACTION: [...]
EXPECTED TRANSITION: [...]
EDGE CASES: [...]
MUST NOT CHANGE: [...]
AUTOMATED PASS/FAIL: [...]
HUMAN FEEL REVIEW: [...].

Source and verification note

This guide was checked against Microsoft Research: Vibe coding and Microsoft Research: Good Vibrations? and Vibe Coding in Software Development: Multivocal Review and updated Sep 5, 2026. The research describes patterns and risks rather than guaranteeing outcomes for every project; validate the workflow in your own codebase.

AI game change-control benchmark

When fixing or extending an AI-built game, separate diagnosis, patching and regression verification. Letting the model redesign architecture while diagnosing one bug makes the result harder to trust and harder to roll back.

Reproduce the behavior.
Classify the layer. Code logic, scene/object state, engine configuration, persistence/networking or test setup.
Protect working systems.
Patch minimally and explain the cause.
Run the target test and neighboring regression tests.

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Acceptance test

The original failure or requested mechanic must be demonstrably correct, the diff must be proportionate to the cause, and protected mechanics must still pass.

Decision rule

Use reproduction → layer diagnosis → minimal change → regression as separate phases.

Change-control matrix

Failure	Evidence	Response
Every fix creates new bugs	Diff touches broad unrelated systems.	Restore the checkpoint and narrow the patch.
Cause is unclear	No deterministic reproduction.	Reproduce before editing.
Scene versus code confusion	Code looks correct but runtime state differs.	Inspect scene/instance state and engine configuration.
Feature regression	New mechanic works but earlier loop breaks.	Run protected-mechanic tests before continuing.

Worked example: stabilize before the next prompt

Suppose adding a hazard causes token spawning to fail. Reproduce the break with a fixed sequence, identify whether the shared score/round state or scene object lifecycle is responsible, and protect the working movement and timer systems. Ask for the smallest causal patch rather than a rewrite.

After the hazard works, replay token collection, respawn, round end and any save/network path sharing the modified state. Only then accept the change and create a new checkpoint. This keeps each iteration attributable to one verified modification.

What to record so the workflow can be reproduced

Keep a compact change record with the starting checkpoint, the exact requested behavior, the files or systems touched, the test you ran, the observed result and any unresolved risk. That record is deliberately tool-agnostic: it lets another developer repeat the same decision even if the model, interface or pricing changes later.

Operational notes for this workflow

Freeze a known-good checkpoint and write a reproduction or acceptance scenario before asking the AI to change the game. For bugs, record the smallest sequence that fails. For a new mechanic, state the exact event that should change state and the behaviors that must remain untouched. Avoid combining bug repair with architecture cleanup.

After the patch, run the target scenario several times and then rerun mechanics sharing the same state, scene objects or interfaces. If the fix requires a larger refactor, make that a separate, explicitly approved task with its own checkpoint and tests.

Primary and official sources checked Sep 7, 2026

Product features, pricing, model behavior and platform controls can change. Re-check the linked official documentation before relying on a current limit, entitlement or interface detail.