NLP

Transformer Architecture for Agentic Language Intelligence: Self-Attention as the Cognitive Layer of Enterprise Decision Systems

How self-attention enables multi-agent context fusion, decision-log comprehension, and hierarchical organizational reasoning

Transformer architectures are central to enterprise language understanding, but production decision systems require additional design constraints. This paper formalizes transformers as the Cognition Layer (Layer 1) of the agentic company stack, introduces cross-agent attention for organizational context fusion, adapts positional encoding to hierarchical coordinates, and outlines training objectives for decision logs, contracts, meeting notes, and specification documents. In evaluated MARIA OS workloads, coordinate-aware attention reduced cross-agent context fusion error by 34% versus standard multi-head attention, and hierarchical positional encoding improved organizational structure extraction F1 by 28%.