AI coding governance for engineering leaders
A governance model for AI-assisted engineering that keeps velocity, code quality, security, and accountability in the same operating system.
Governance should be operational
Policies only help when they show up in everyday engineering work. We translate governance into task categories, review standards, model usage rules, and measurable adoption checks.
The DRO control model
Teams decide what to delegate, what to review, and what to own. This keeps AI work moving while protecting architecture, security, data handling, and business logic decisions.
What leaders can measure
Useful metrics include review time, escaped defects, cycle time, agent run abandonment, test coverage movement, and the percentage of AI-assisted work with explicit verification.
Related training topics
Bring this into your team
We tailor the training to your codebase, adoption stage, and review standards.
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