How to Lock Working Game Systems Before Asking an AI Agent to Add a New Feature
Brightory creates practical AI guides and workflows based on research, testing, and documented AI usage patterns.
Create a protected-systems manifest with behavior contracts, tests, and a write-scope list. Tell the agent it may inspect protected code but cannot modify it unless it first proves the new feature cannot be implemented at the intended boundary.
Create a protected-systems manifest with behavior contracts, tests, and a write-scope list. Tell the agent it may inspect protected code but cannot modify it unless it first proves the new feature cannot be implemented at the intended boundary.
A lock is behavioral, not only file-based
You may need an agent to read a working movement controller while building stamina. Mark movement as protected by behavior: speed, jump arc, input mapping, public methods, and tests. That prevents a “small helper” change from quietly altering feel.
Use read-many, write-few
Agents need context to integrate features, but context should not imply permission. Give broad read access to understand dependencies and narrow write access for implementation.
Require an exception request
If the agent believes a protected system must change, its next response should stop and explain the exact dependency, the minimal proposed interface change, and the regression tests it will run. Human approval turns hidden coupling into an explicit decision.
Keep the manifest in the repository
Do not rely on chat memory. A short `PROJECT-CONTRACTS.md` or similar file can list protected interfaces, tests, and system ownership so the rule persists across agents and sessions.
Working template
PROTECTED SYSTEM: [...] BEHAVIOR CONTRACT: [...] PUBLIC INTERFACE: [...] REGRESSION TEST: [...] NEW FEATURE WRITE SCOPE: [...] RULE: protected code is read-only unless an exception request is approved.
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.
Working on a similar coding task? Use the free Coding Agent Context Builder to turn scope, relevant files, protected areas and verification into an agent-ready handoff.
Want to make this workflow repeatable?
If lock Working Game Systems Before Asking an AI Agent to Add a New Feature is something you need to do more than once, the expensive part is rebuilding the process every time. The Brightory AI Game Builder Workflow Kit packages this kind of work into reusable planning sheets, prompt structures, change-control templates, playtest checks and handoff assets, so you can carry the method into the next project instead of starting over.
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.
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.
Final production checklist
- Checkpoint: is there a known-good version you can return to?
- Behavior: is the requested mechanic or decision stated in observable terms?
- Scope: are protected systems and out-of-scope features named?
- Test: is there a repeatable playtest or buyer comparison benchmark rather than a subjective “looks good” judgment?
- Evidence: are platform features, pricing or rights linked to current primary sources where those facts matter?
Do not move to the next prompt, feature or purchase decision until the current step can be explained from this record. That discipline is what turns a one-off AI result into a workflow another developer can reproduce.
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.