Domain-Driven Agents
The author argues that LLMs struggle in legacy software environments because they lack the context of existing technical debt and inconsistent coding standards. Instead of blaming the models, the author suggests that developers must incrementally improve code quality and documentation to make systems 'ready' for AI integration.
Why it matters
As companies rush to integrate AI into aging software, this perspective highlights the necessity of human-led refactoring over blind automation.
I've been using LLMs heavily in the last years in coding, or more generally, in software engineering. I watched many times what productivity boost I could gain from it, and I used LLMs in more and more of my projects. It works well in greenfield projects, and small ones. The reality is that in day to day work we need to introduce agents into legacy codebases with heavy dependency trees, strong coupling, and a tech debt backlog full of everything we never got to. We quickly notice that the quality of work LLMs can deliver drops sharply.
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