AI code review governance for engineering leaders
Governance fails when it lives in a policy doc instead of the daily pull request. Engineering leaders need an operating model that turns AI code review into a habit: clear LLM code review standards, defined agent permissions for coding agents, and MCP boundaries reviewers can enforce. We build that governance model into task categories teams actually use, with adoption checks that show whether it holds.
Governance should be operational
Policies only help when they show up in everyday engineering work. We translate governance into task categories, AI code review standards, LLM code review rules, MCP boundaries, coding-agent permissions, 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.
Official references
Current product documentation we use when shaping this training topic.
Selected research
Representative field notes connected to this topic.
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