Are coding agents much better at starting projects than fixing real codebases?

Reddit r/AI_Agents News

Summary

An observation that coding agents perform well on new projects but often struggle with existing codebases, where the need for minimal changes and understanding of hidden dependencies limits their effectiveness.

One thing I keep noticing with coding agents: They feel amazing when the project is new. You ask for an app, a feature, a script, a dashboard and suddenly there is working code. But existing codebases are different. There are old decisions, weird naming, hidden dependencies, half-documented logic, tests nobody trusts, and files that should not be touched unless you really understand the system. That is where agents start to feel less magical. Not because they cannot write code. Because real engineering is often about changing the smallest possible thing without breaking everything around it. A greenfield task rewards generation. A brownfield task rewards restraint. For people using coding agents in real projects: Do you trust them more for building new things? Or for modifying existing code? And what makes them fail most in older codebases?
Original Article

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