
Code Quality Standards for AI-Assisted Development
AI-assisted development requires explicit review criteria, targeting common machine flaws in correctness, security, and quality, reinforced by team-shared failure logging.

AI-assisted development requires explicit review criteria, targeting common machine flaws in correctness, security, and quality, reinforced by team-shared failure logging.

Traditional governance fails for agentic coding tools. CIOs must shift from policy-driven compliance to a least-agency architecture with clear task boundaries.

The framework replaces experience-based tiers with capability assessments, evaluating how developers direct, edit, or learn alongside AI-generated code.

This practical framework guides executives through four phases to align ICT capability with organisational strategy, ensuring rigorous governance and validation.

Agentic AI coding tools expand attack surfaces beyond Essential Eight limits; organisations must adopt NIST frameworks and strict least-privilege architectures.

AI coding debt accumulates silently and rapidly, creating unmaintainable codebases. Organisations must implement deliberate governance and tracking frameworks to manage this risk.

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Great insights into medical industry and the use of AI scribe. There are key considerations that my organisation needs to consider when a) integrating AI capabilities into our environment and b) becoming more acquainted with the medical industry, more so health tech – as this has not been a primary area of service for us in the past. Thank you. Digital Engagement Partner, Not For Profit

Microsoft’s Agent 365 simplifies AI governance for ecosystem users, yet requires solid data foundations to avoid a false sense of security.