| Target Entities | AI Factory, Software Delivery, Release Stabilization, Acceptance Criteria, Protected Checks, Negative Controls, Human Release Approval |
|---|---|
| Core Value | Smaller, frequent changes accepted on reviewed evidence, protected checks with negative controls, release approval kept separate from accepting a change |
| Tech Stack | Acceptance criteria, Protected checks, Negative controls, Evidence review, Separate release approval |
Software & AI
How the team behind an internal game-testing product shortened release stabilization with the AI Factory method
An internal game-testing product had a delivery bottleneck: stabilization before a release took weeks, and changes arrived in infrequent batches. We ran its development under the AI Factory delivery method, with human-defined acceptance criteria, protected checks and human approval for deployment. In reported internal, directional results, stabilization before a release fell from weeks to about a day.
release stabilization, down from weeks (reported internal, directional)
features released, instead of infrequent batches (reported internal, directional)
engineers on the product (reported internal, directional)
Challenge
Stabilization before a release took weeks, and changes arrived in infrequent batches.
What we built
Smaller changes under the AI Factory delivery method, with acceptance criteria, protected checks and separate human release approval.
Result
In reported internal, directional results, release stabilization fell from weeks to about a day, with several features released per day.
The challenge
The product helps game studios replay important player journeys and review findings with gameplay video. Its own development had a delivery bottleneck: stabilization before a release took weeks, and changes arrived in infrequent batches. The team needed a repeatable way to ship smaller changes while keeping control over correctness and release decisions. More frequent output had to come with evidence people could review.
What we built
- Started each change from human-defined intent and acceptance criteria. Coding agents then built and tested it within approved tools, limits and a controlled path for accepting code.
- Tested the intended behavior with protected checks, and used negative controls to confirm that a deliberately incorrect result would be rejected. The team reviewed that evidence against the agreed criteria before accepting a change.
- Kept autonomy selective: only changes with trustworthy checks could qualify for less manual review. Authentication, billing, migrations and contracts continued to require a person to review the change, and accepting a change stayed separate from approving a release.
How the data flows
Set by people
Evidence and approval
The outcome
In reported internal, directional results, stabilization before a release fell from weeks to about a day. The same results show several features released per day instead of infrequent batches, and the team on the product going from six engineers to two. These figures come from the product's own development and illustrate a controlled delivery approach, not a guaranteed outcome for another team.
Systems & technologies
Before vs after
Get in touch
Do your releases wait on weeks of stabilization?
We can set up the AI Factory delivery method around one workflow in your codebase, with acceptance criteria, protected checks and separate human release approval.
Email us directly
sales@3alica.com