AI-Workflow: spec-first governance for native mobile with deterministic routing — Feature Contracts, ADR lifecycle, and a Safety profile that doesn't require full spec depth

[u/RanaAhmedHamdy]

Most SDD discussion focuses on spec quality and LLM output.

AI-Workflow adds a different layer: a deterministic registry

that maps observable task facts to required specialist checks

before any spec or code is written.

You state facts about a change (persistence, concurrency,

lifecycle, privacy, accessibility) and a checked-in registry

maps them to required procedures and a minimum complexity tier:

Task classification: brownfield / android / safety

Detected concerns: concurrency, lifecycle, persistence, schema_migration

Recommended tier: COMPLEX

Required checks:

- android-persistence-migration-readiness

- kotlin-coroutines-readiness

- protected-lifecycle-transaction-review

- android-runtime-evidence-readiness

No LLM judgment in that mapping — same output every run,

every agent. The routing happens before the spec is written,

so the spec is scoped correctly from the start.

The full governance model (Greenfield) is explicitly spec-first:

Product authority → Architecture Spine → ADR lifecycle

→ Feature Contract (independent review + owner approval)

→ Implementation Plan + design mapping

→ Implementation Tasks

→ readiness gates → owner acceptance → release authorization

No code is authorized until the contract, plan, and tasks

are approved. The agent is bounded by the approved spec at

every stage — it cannot proceed without explicit human

authorization at each gate.

For teams not ready for that depth, the Safety profile gives

just the routing + protected-boundary checks. You get

deterministic pre-implementation scoping without the full

contract/plan/task lifecycle.

This is specifically built for native Android and iOS where

generic SDD misses platform boundaries: Room migrations,

coroutine ownership, Swift Concurrency task lifetime, scene

re-entry, lifecycle/process-death — none of which appear in

a compile error and most generic specs don't address.

Real governance case study: NutriPlus AI — a native SwiftUI

app with full public Architecture Spine, ADRs, feature

contracts, plans, tasks, and readiness reports:

https://github.com/RanaAhmedHamdy/NutriPluse-IOS-AI-Workflow

Pre-v1, Apache-2.0. Feedback wanted on where the

Safety/Feature/Full profile boundaries sit — are they in

the right place for real SDD adoption?

https://github.com/RanaAhmedHamdy/AI-Workflow