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  • Book Overview & Buying AI Agents in Practice
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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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Part 1: Foundations of AI Workflows and the Rise of AI Agents
4
Part 2: Designing, Building, and Scaling AI Agents
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Part 3: Road to an Open, Agentic Ecosystem
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Index

Designing, Building, and Scaling AI Agents

In this part, we dive into the practical aspects of designing, developing, and deploying AI agents. Building on the foundational concepts introduced in Part 1, you’ll now explore how to structure intelligent, goal-oriented systems using modern orchestration frameworks, memory modules, and tool integrations.

We begin by understanding the role of an AI orchestrator—a central component that coordinates tasks, tools, and memory to enable complex workflows. You’ll get familiar with the most popular orchestrators in the market and learn how to select and configure one for your needs.

The chapters then focus on managing memory and context, which are essential for making AI agents persistent, adaptive, and capable of handling multi-turn interactions. We’ll also cover how to extend agents with external tools and APIs, enabling real-time data access, integration with enterprise systems, and broader actionability...

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AI Agents in Practice
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