Most of the time a human works over code multiple times, and still produces tech debt.
Give an AI agent enough time, by prompting it multiple times, and explicit instructions to look for and address tech debt of various forms, and it will.
In my experience, an agent will rarely recognise a common pattern and lift it into a new abstraction. It requires a human with taste and experience to do it. For example, an agent will happily add a big amount if branches in different places of the codebase where a strategy pattern or enum would be better (depending on the language).
If you have a working prompt or harness that ameliorates this, I'd be glad to see it.
Maybe you can describe what the various forms of tech debt are that you are talking about?
There is no need to improve their mental model of a problem and ability to code to recognise the refactoring opportunities that already exists in the code. It only takes a sufficient skill, and effort invested on refactoring. The way to get a model to invest that effort is to ask it. As many times as you're willing to.
> Maybe you can describe what the various forms of tech debt are that you are talking about?
Any. Whether or not you need to prompt much to address it depends on consistency. In general I have a simple agent whose instructions are just to look for opportunities to refactor, and do one targeted refactor per run. All the frontier models knows well enough what good looks like that it is unnecessary to give it more than that.
The best way of convincing yourself of this, is to try it. Ask Claude Code or Codex to "Explore the code base and create a plan for one concrete refactor that improves the quality of the code. The plan should include specific steps, as well as a test plan." Repeat as many times as you care to, or if in Claude Code, run /agents and tell Claude Code you want it to create an agent to do that. Then tell it to invoke it however many times you want to try.
