I've been writing software for a while. I'm comfortable at every layer of the stack. When AI started becoming something you could actually ship into production applications, I did what most engineers do: I built it myself.

First project, not bad. Pick a model, call the API, handle the response. Clean enough. I understood exactly what was happening at every step.

Then the requirements got more complex. I needed multiple models in the same workflow. I needed a document parsing layer upstream of the LLM. I needed the output to land in a database instead of just getting returned to the client. Suddenly I was writing a lot of code that had nothing to do with the problem I was actually trying to solve. Glue code. Wiring. Infrastructure that existed purely to move data between components that were never designed to talk to each other.

I accepted that as the cost of doing business. This is just what building AI features looks like, I told myself.