Posted on in Programming
An AI coding agent does not need a clever prompt. It needs an assignment that a reviewer could recognize after the fact.
That distinction matters. “Fix the flaky test” may be enough for a human who already knows the service, its constraints, and the team’s habits. To an agent …
Posted on in Programming
AI is very good at making CI failures feel less lonely.
That is useful. A large CI log can be hostile terrain: thousands of lines of setup output, dependency chatter, repeated warnings, retry noise, test framework boilerplate, and one real clue hiding near the bottom. Asking an AI tool to …
Posted on in Programming
AI coding agents can produce more code than your review process can absorb.
That is the useful part and the dangerous part.
The same agent that can trace a bug across five files, update tests, adjust documentation, and clean up a few nearby rough edges can also turn a small …
Posted on in Programming
AI coding agents are very good at refactors until they are not.
That is the uncomfortable part. The same agent that can rename a helper across a repository, split a giant function, update tests, and clean up repetitive call sites can also make one tiny semantic change that hides inside …
Posted on in Programming
AI-written tests are dangerous in exactly the way good-looking tests are always dangerous: they can make you feel safer without actually reducing much risk.
That is not an argument against using AI coding agents to write tests. I use them for test scaffolding, edge-case enumeration, fixture cleanup, and regression coverage …
