I 100% agree in the importance of the data model, and the examples show that it often makes more sense to start from the user's perspective of it rather than your db schema.
Interestingly AI Agents are all about disrupting the hard bounds of existing data and interaction models and it turns out the lowest common denominator is often the winner. Eg.: file system > database, grep > embeddings, markdown > pdf, generative ui > portals, computer use > api-s etc.
There simply is no need for all that abstraction / interface / infrastructure to eg. answer questions about documents or to keep track of todo lists, workflows or sending messages etc. when you have glue that can translate between the data models.
Interestingly AI Agents are all about disrupting the hard bounds of existing data and interaction models and it turns out the lowest common denominator is often the winner. Eg.: file system > database, grep > embeddings, markdown > pdf, generative ui > portals, computer use > api-s etc.
There simply is no need for all that abstraction / interface / infrastructure to eg. answer questions about documents or to keep track of todo lists, workflows or sending messages etc. when you have glue that can translate between the data models.