November 2026
Intermediate
35 pages
29m
English
Overview
AI coding tools can produce software faster than teams can confidently review it. As output continues to accelerate, familiar safeguards around quality, security, consistency, and accountability are beginning to strain. Code Governance for AI-Native Software Development gives engineering leaders a practical approach for closing that gap and maintaining confidence in what reaches production.
This report explains why traditional review practices and smarter models alone cannot keep pace with AI-native development. It explores a new quality layer built on organizational context, managed standards, specialized agents, and institutional memory, introducing Artificial Wisdom as a way to bring those elements together. Readers will learn how to rethink oversight across the SDLC and create shared controls that scale with both human- and AI-authored software.
- Identify risks introduced by AI-generated code
- Assess whether current review practices can scale
- Define requirements for an enterprise quality layer
- Apply organizational standards across the entire SDLC
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