doc/AGN/03_Differentiators_from_Traditional_AI.md

Differentiators from Traditional AI

1. Enterprise-Focused Design

AGNs are built with an enterprise-focused mindset, designed to solve real business problems rather than simply excel in abstract mathematical challenges. This differentiates AGNs from other AI models that typically lack the contextual and domain-specific understanding needed in practical settings.

2. Structured, Contextual Reasoning

AGNs excel in structured, contextual reasoning. Unlike transformers and LSTMs, AGNs emphasize structured relationships and attribute-based decision-making. This makes AGNs suitable for applications that require deep, multi-domain contextual understanding.

3. Real-Time Learning and Adaptation

AGNs are designed to update and adapt relationships without retraining, which sets them apart from static models. This makes them highly suitable for environments where data changes continuously, and real-time learning is crucial.