AI Agents: The Definitive Guide

Preface

When I first started working with transformers in 2019, AI agents, at least in the form we understand them today, were not yet the central topic they have since become. I was immediately drawn to the transformer architecture itself: the elegance of attention, its versatility, and the way the same underlying principles could extend across language, vision, audio, time series, and other modalities.

Two years later, I built my own attention-based architecture around specialized modules that activate selectively, operate independently, and communicate only when the context requires it. Although I designed the model for dynamic prediction, the questions behind it now feel strikingly familiar: which components should act, what information should they share, and how should context shape what happens next? What continues to fascinate me is how those same ideas have since moved from individual model architectures into complete agentic systems, where models reason, use tools, maintain state, interact with their environments, and coordinate with other agents. The level of abstraction has changed, but many of the underlying principles remain the same.

My own work continues to evolve alongside that shift, from attention-based and multimodal architectures toward coordination, adaptive systems, and AI agents. Today, my research and development focus on how these systems can learn and improve through reinforcement learning, search, memory, and adaptive coordination, a direction that also ...