What developers get wrong about AI code agents (and how to fix it)

What developers get wrong about AI code agents (and how to fix it) AI code agents are now good enough to write non‑trivial features, refactors, and migrations—but most teams adopt them like a smarter autocomplete. They paste a big prompt, hope the agent “gets it,” and then judge the whole category by the first broken PR. That’s the wrong mental model. An AI code agent is less like a senior engineer and more like a fast junior engineer with (a) perfect recall of public patterns, (b) imperfect understanding of your codebase, and (c) the ability to take actions very quickly. If you don’t wrap that speed in constraints, you get speedrunning toward subtle bugs. The good news: reliability is mostly fixable with workflow design. The model matters, but your process matters more. This matters now because agents are moving from “suggest” to “do”: they run commands, open files, edit multiple modules, and push diffs. As soon as a tool can change your repo, you need the same discipline you apply to