In the Era of Artificial Intelligence: How the Transformer Model is Changing the Structure of AI Agents

Abstract

Transformer’s self‑attention and parallel processing have reshaped how AI agents are built. This paper proposes a three‑level taxonomy—shallow, deep, and system‑level integration—to show how the Transformer redefines perception, planning, and multi‑agent coordination. Together, these layers turn agents from simple sensor‑to‑action systems into reasoning, collaborating, and continuously adapting intelligences. The analysis combines a comparison of representative models, a walk through application scenarios, and an examination of key bottlenecks that remain open.