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AI Agents in Practice
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In Part 1 of this book, we explore the evolution of AI development since the rise of large language models (LLMs) in late 2022, leading up to the emergence of AI agents as a new architectural paradigm.
This part begins by tracing how AI workflows have shifted—from simple API calls to more dynamic systems such as retrieval-augmented generation (RAG)—and highlights key technical breakthroughs such as fine-tuning, model distillation, and reinforcement learning from human feedback (RLHF). It introduces the concept of agentic behavior as the next frontier in building intelligent, goal-driven systems.
We then dive into what makes an AI agent: how it differs from traditional automation, the components that enable it (LLMs, tools, memory, and knowledge), and why the field is rapidly converging on more autonomous, interactive systems.
You’ll also gain an understanding of how AI agents build on the foundation of LLMs while incorporating new layers of intelligence and orchestration, setting the stage for more personalized, persistent, and task-oriented AI applications.
This part contains the following chapters: