Overview

AI is no longer just about models. It is about systems. In recent years, a new paradigm has emerged: AI agents - systems that can perceive, decide, act, and adapt over time. From coding assistants and workflow automation to decision-making systems and autonomous environments, agents are reshaping how intelligence is built and deployed. But why do some agents succeed while others fail? This book answers that question by analyzing 20 representative agent systems across real-world arenas, including hackathons, benchmarks, and interactive environments. Rather than focusing on tools or models, it reveals the underlying structure that drives performance. Inside, you will discover: - A unified framework for understanding all agent systems - Detailed case studies across rule-based, coding, workflow, decision, and autonomous agents - A system-level perspective on feedback loops, memory, and control - Practical insights into designing stable, high-performance agents The central thesis is simple: The future of AI will not be defined by better models, but by better systems. Whether you are an engineer, researcher, or builder, this book provides a structured way to think about AI agents - not as tools, but as decision systems operating under uncertainty.

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