AI Engineering Operating System · v1

Repo Implementation Starting Line

Scaffold the repository with a small set of durable contracts, templates, and one complete feature example. The goal is not merely to ship code—it is to create a repeatable system that lets humans and specialized AI agents deliver reusable platform capabilities consistently.

Charter

→

OpenSpec

→

Playbook

→

Agents

→

Validation

→

Human Feedback

→

Learn + Improve

Milestone 01

Scaffold the Core

Land the minimum repo structure required for a repeatable AI-native engineering workflow.

Project charter
OpenSpec template
Playbook template
Artifact schemas
Validation structure
Agent role definitions

Milestone 02

Ship One Full Example

Use a real feature to prove the operating model end to end.

Player can start, pause, and resume a game

Game lifecycle State management Human QA Platform reuse

Milestone 03

Automate the Playbook

Once the artifacts work manually, begin wiring specialized agents into the workflow.

Codex executes OpenSpec lifecycle
Playbooks generate work packages
Agents implement and test
Humans approve experience gates
Feedback re-enters Explore

Proposed Repository Skeleton

Platform-first monorepo

/
├── README.md
├── AGENTS.md
├── charter/
│   └── charter.md
├── specs/
│   ├── templates/
│   │   └── feature-spec.md
│   └── features/
├── playbooks/
│   ├── templates/
│   └── new-feature/
├── agents/
│   ├── chief-architect/
│   ├── gameplay-engineer/
│   ├── embedded-engineer/
│   ├── qa-test-engineer/
│   └── engine-steward/
├── artifacts/
│   └── schemas/
├── knowledge/
│   ├── adr/
│   ├── patterns/
│   └── lessons/
├── validation/
│   ├── automated/
│   ├── e2e/
│   └── human/
├── platform/
│   ├── engines/
│   ├── packages/
│   └── firmware/
├── apps/
│   ├── ios/
│   ├── android/
│   └── web/
├── services/
│   ├── python/
│   └── node/
└── tests/

Required OpenSpec Standard

Every feature strengthens the platform

Each feature spec includes a mandatory Platform Impact section.

Reusable components created or extended
Shared APIs and engine impact
Agent or playbook updates
Knowledge captured
Future reuse opportunities
Platform ROI: what capability was created, and who benefits next?

Reference Feature

Start · Pause · Resume

User story: As a player, I want to start, pause, and resume a game without losing progress.

Pause is immediate and stable
Resume restores the correct state
Audio and input lifecycle remain consistent
State handling avoids corruption
RP2350 memory and latency overhead stay low
Human experience review is explicitly approved

Reusable Platform Outcome

Capabilities promoted from the feature

Game State Manager
Pause / Resume State Machine
Input Suspension Service
Audio Lifecycle Manager
Shared Game Lifecycle API
Lifecycle test patterns for future games

Definition of Done · v1

A new AI agent—or a new human contributor—can enter the repository, follow the New Feature playbook, use the required artifacts and gates, and ship a working feature with code, tests, documentation, human feedback, and a clearly identified reusable platform outcome.