devbooks-delivery-workflow

Orchestrating AI-driven software delivery requires a deterministic workflow that routes tasks through Proposal, Design, Spec, Plan, Test, Implement, Review, and Archive stages with strict evidence-backed gate checks. This enforces auditable artifacts and role isolation across the lifecycle.

SSOT routing in change management delegates tasks to sub-skills through a minimal sufficient closure, ensuring single-source-of-truth governance. It enforces evidence-backed gates and traceability across all lifecycle artifacts for accountable AI-assisted software delivery.

Enforcing role isolation for AI-generated code requires artifact-centric checks that strictly separate Test Owner, Coder, and Reviewer responsibilities. This governance workflow ensures accountable deliverables and evidence-backed traceability across implementation stages.

Yes, running an automated delivery workflow with evidence gathering requires Git, Node.js, Python3, and ripgrep. These dependencies support the orchestration scripts that generate auditable artifacts and enforce traceability across the delivery lifecycle.

Yes, you can bootstrap an AI delivery workflow when upstream truth data is missing. The orchestration handles bootstrap scenarios and complex change packages by applying deterministic governance flows to establish evidence-backed artifacts.

Ensuring traceability across complex change packages requires an end-to-end orchestration workflow that applies SSOT-driven governance and evidence-backed gates. This generates auditable artifacts across all stages from Proposal to Archive.