AI coding agents are getting very good at writing code.
They can build components, create APIs, fix bugs, and implement features from short prompts.
But I kept noticing one issue:
Working code does not always mean a good product.
For example, if you ask an agent: “Add a delete button to every project.”
It may technically do exactly that.
But will it also think about:
confirmation before deletion
error handling
undo options
accessibility
clear feedback to the user
Those are not just coding problems. They are product judgment problems.
That led me to experiment with a reusable instruction layer for AI coding agents at AudranLab.
The idea is simple:
Instead of only asking an agent, “Can you build this?”, also encourage it to ask, “Is this a good way to build it?”
I want agents to consider things like accessibility, failure states, destructive actions, usability, and sensible defaults while they work.
This does not magically turn an AI into a product designer.
But I think it raises an interesting question:
Can explicit product principles consistently improve the quality of software generated by coding agents?
That is what I’m currently exploring.
My next step is to test the approach across different coding tasks and compare the results with and without the additional product-judgment layer.
If you’re interested in AI agents, LLM reliability, developer tools, or applied AI, I’ll be sharing more experiments here.
AudranLab: [https://www.audrantechlab.online/](https://www.audrantechlab.online/)