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Maintaining software quality with AI
How a team keeps a codebase coherent, owned, and understood once an AI agent is writing a large share of the diffs: why individually reasonable changes compound into architectural drift, why a human still has to own the system's shape even as the model does more of the typing, the concrete guardrails (architecture docs, module ownership, mechanical enforcement) that hold the line, and how to measure whether a team's understanding is keeping pace with what it ships.
Questions in this set
- What quality risk does AI-assisted development create?
- Why does architecture drift when an agent writes most of the code?
- Why does a system still need a human owner if an agent writes the code?
- How do architecture docs keep an agent's defaults aligned with your codebase?
- Who reviews a change when an agent could touch any file in the repository?
- How do you enforce a convention without depending on someone remembering it?
- Why can code pass every check and still be quietly getting worse?
- How do you know whether AI is compounding your team's capacity or its debt?
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