ldraw-nova: AI agents build LEGO models by writing Python geometry
In ldraw-nova, models write a plan and a Python generator instead of raw coordinates, but nothing checks physics or stability.
Most of Cursor's agent spend sits in growing context, not the cached system prompt; on-demand tools and cache breakpoints moved the bill.

Cursor cut about two thirds of its agent's system prompt. Total token cost fell 7%.
That gap is the useful part.
The system prompt is the part a team fully controls, so it gets trimmed first. It's also resent every turn and mostly served from cache. The rest of the bill sits in what piles up: file reads, tool output, a conversation that keeps growing.
What moved their number:
If you run your own agent harness, split spend by layer before you trim anything.
Trim the prompt for hygiene. Trim the context for money.