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AI Agents in Practice

AI Agents in Practice

By : Valentina Alto
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AI Agents in Practice

AI Agents in Practice

5 (1)
By: Valentina Alto

Overview of this book

As AI agents evolve to take on complex tasks and operate autonomously, you need to learn how to build these next-generation systems. Author Valentina Alto brings practical, industry-grounded expertise in AI Agents in Practice to help you go beyond simple chatbots and create AI agents that plan, reason, collaborate, and solve real-world problems using large language models (LLMs) and the latest open-source frameworks. In this book, you'll get a comparative tour of leading AI agent frameworks such as LangChain and LangGraph, covering each tool's strengths, ideal use cases, and how to apply them in real-world projects. Through step-by-step examples, you’ll learn how to construct single-agent and multi-agent architectures using proven design patterns to orchestrate AI agents working together. Case studies across industries will show you how AI agents drive value in real-world scenarios, while guidance on responsible AI will help you implement ethical guardrails from day one. The chapters also set the stage with a brief history of AI agents, from early rule-based systems to today's LLM-driven autonomous agents, so you understand how we got here and where the field is headed. By the end of this book, you'll have the practical skills, design insights, and ethical foresight to build and deploy AI agents that truly make an impact.
Table of Contents (15 chapters)
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1
Part 1: Foundations of AI Workflows and the Rise of AI Agents
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Part 2: Designing, Building, and Scaling AI Agents
10
Part 3: Road to an Open, Agentic Ecosystem
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Index

Foundations of AI Workflows and the Rise of AI Agents

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:

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AI Agents in Practice
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