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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
10
Part 3: Road to an Open, Agentic Ecosystem
14
Index

Understanding and designing different workflows for your multi-agent system

When we transition from single-agent systems to multi-agent architectures, the way agents interact becomes crucial. Different workflows (or, in other words, communication patterns) shape how agents collaborate, share information, and make decisions. Selecting the right workflow depends on the nature of the task, the roles of each agent, and the desired outcomes.

Let’s explore five core multi-agent workflows (see Figure 7.3):

Figure 7.3: Different types of agentic workflows

Figure 7.3: Different types of agentic workflows

Here are the five core multi-agent workflows in detail:

  • Network: All agents are peers in a fully connected graph, where each agent can directly communicate with any other. This allows for highly interactive, dynamic collaboration.

Let’s consider the following example. In a fast-moving start-up, a team of AI agents collaborates to conceptualize, validate, and plan a new mobile app launch...

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AI Agents in Practice
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