When an agent recommends a hotel or a vendor, that recommendation arrives carrying the accumulated preferences of people who never thought of themselves as tastemakers.
The chain is long and almost entirely invisible. Data engineers decide what counts as quality training material. Gig workers in Nairobi and Manila label outputs under time pressure, encoding snap preference judgments into reward signals. ML engineers collapse those varied preferences into a single optimization score. Product managers set default parameters and tone guidelines. Enterprise admins scope which tools and catalogs the agent can access at runtime.
Every link is a quiet editorial decision dressed up as a technical one. Nobody in the chain thinks they're deciding what "good" looks like. Every single one of them is.