Using ESLint and static analysis as maintainability sensors for AI coding agents
By
Birgitta Böckeler
If you only eat one bagel today, this is the bagel.
Summary
A practical walkthrough of using computational sensors like ESLint and static analysis tools to monitor and improve codebase maintainability. The article defines maintainability as making it easy and low-risk to change code over time (internal quality), and explores how coding agents and AI-assisted development can benefit from automated feedback loops to detect early signs of degradation in code quality, architectural fitness, and functional correctness.
Key quotes
· 4 pulledI define maintainability here as making it easy and low risk to change the codebase over time - also known as 'internal quality'.
So I don't only want to be able to make changes quickly today, but also in the future.
And I don't want to worry about introducing bugs or degradation of fitness every time I make a change - or have AI make a change.
I usually see the first signs of cracks in the
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