The AI Re-Think Blog

Autonomous agents can reason, plan, and execute. Context graphs give them institutional knowledge. But neither technology alone creates an AI system that gets better at its job over time. This paper introduces compounding intelligence — the architectural pattern that connects agents, graphs, and a feedback loop that enables self-improving judgment. Grounded in transformer attention theory and validated through four controlled experiments, it explains how to build enterprise A

Canonical reference for the mathematical framework connecting transformer-style attention mechanisms to cross-graph discovery in enterprise AI systems. Includes experimental validation across four controlled experiments. Abstract We present a formal mathematical framework connecting cross-graph discovery in enterprise AI systems to the scaled dot-product attention mechanism of Vaswani et al. (2017). The correspondence operates at three levels: (1) a single-decision scoring ma