ROMA changes the frame from “How big is your model?” to “How well does your system coordinate?”

One model trying to do everything forces linear workflows.
ROMA breaks the work apart and routes it through a graph of smaller agents, each with a defined role: retrieval, reasoning, verification, execution.

Three compounding effects show up fast:

+ complex tasks stop overwhelming the system
+ parallel execution increases surface coverage
+ auditable components make improvement predictable

This is why orchestration beats raw scale.
Better coordination produces better results than marginal gains in model quality.

And this is the direction @SentientAGI is leaning into as well; shifting the frontier from raw parameter count to the architecture that directs how intelligence is assembled.

Zoom out and the macro shift is obvious:
AI advantage moves from compute-heavy to workflow-heavy.

Models converge.
Orchestration differentiates.

ROMA is that differentiation layer.