AI coding tools change how fast code gets written. They don't change what makes a system trustworthy. These are some thoughts on the parts of engineering practice that were already worth doing well, and that matter more, not less, now that a meaningful share of the code in front of you was written by something other than a person.
A build pipeline that reliably gates every change was always the safety net. It's the only thing standing between an AI-generated regression and production now.
read →Automated triage is only as good as the taxonomy and judgment calls it's built on, and now that judgment gets enforced at scale instead of occasionally.
read →Before anyone, human or AI, touches a legacy system's internals, you need a reliable way to know if its behavior changed. Characterization tests are how.
read →AI tools are excellent at turning a clear spec into code, and bad at inferring intent from vibes. Requirements work just became the actual leverage point.
read →When you can't personally read every line of an AI-authored change, a real test plan, with traceability back to requirements, becomes the review mechanism.
read →Unit, integration, and system tests each catch a different class of failure. AI-assisted changes tend to fail at exactly the level a human-paced team stops watching.
read →Language and platform choice used to be high-stakes because migrating later was expensive. With the rest of the discipline in place, that math has changed.
read →A stale comment used to cost a skeptical engineer twenty minutes. An AI assistant takes it at face value and builds on it. Documentation needs an owner, a review, and a staleness check, like a test suite.
read →Asking an AI assistant to check its own work runs the same judgment twice. A model from a different provider doesn't carry the same blind spot into the review.
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