AI-DLC documentation

AI-DLC governs accountable AI-assisted delivery. Workbench operationalizes the model inside client-controlled engineering environments.

Start with The AI-DLC operating process to understand the delivery pod, Workbench platform, client counterparties and the path from intent to reusable learning.

Course

• Course goals explain how the course builds shared intuition, a common vocabulary and lasting work habits.

Tutorials

• Follow one feature through Define, Plan, Execute, Judge and Learn.
• Run a governed pilot with one client-owned feature.
• Establish readiness when inputs or roles are incomplete.

How-to guides

• Set up an AI-DLC delivery pod
• Establish pod ceremonies
• Define a feature
• Assign accountable roles
• Assemble a confidence package
• Recover a feature

Reference

• Lifecycle
• Roles and claims
• Ceremonies and systems of record
• Pod RACI
• Evidence and decision states

Explanation

• The AI-DLC operating process
• AI-DLC and Workbench
• Evidence is not acceptance
• Controlled recovery

An author does not ratify their own consequential claim. A passing gate cannot accept a feature, approve a release or declare production readiness.