is “attention” the missing layer in production ML systems?

been thinking about this after working around a few ML systems in production.

a lot of focus goes into improving models — architecture, fine-tuning, evals — but it feels like less attention goes into how model outputs actually get used.

in practice, the breakdown isn’t always model quality.

it’s things like:

• high-signal outputs getting lost in streams of low-priority events
• predictions arriving after the window to act has already passed
• no clear routing of outputs to the right decision-maker or system
• lack of memory around past decisions + outcomes

so even when the model is technically “correct,” it doesn’t lead to action.

in transformers, attention mechanisms explicitly determine what matters. but at the system/org level, that concept feels underdeveloped.

it almost feels like there’s a missing layer between inference and action — something that continuously decides:

• what signals matter right now
• who/what should receive them
• and how they should influence decisions

i’ve seen a few people start to refer to this loosely as “attention infrastructure,” but not sure if that’s an actual emerging pattern or just a framing.

curious if others here have run into similar issues, or if there are existing system designs/tools that already solvw for this and i’m just not aware!!