Your AI agent can read your codebase. It doesn't know your product.

"AI coding agents can grep your codebase. They still produce generic-SaaS output because they lack the design context of your product: how it behaves, which interaction patterns it rejects on principle, what makes it feel like yours."

"The problem was a different shape of knowledge: what the product is, from a design perspective. How it behaves. Which interaction patterns we reject on principle. How it speaks."

"None of that lives in the code. It lives in taste, in a Figma file, in how we decided to frame a feature in Slack last Tuesday, in a half-dozen people's heads."

"The problem isn't what the agent can read. It's what the code doesn't say."

AI coding agents can analyze codebases but often produce generic outputs due to a lack of understanding of a product's design context. This context includes how the product behaves, the interaction patterns it rejects, and its brand identity. For example, an AI agent suggested features that did not align with the core interaction of a product, highlighting the gap in knowledge. The essential design context resides outside the code, in team discussions and design files, which the AI cannot access.

Read at Medium