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AI Agents in Practice
AI Agents in Practice

AI Agents in Practice: Design, implement, and scale autonomous AI systems for production

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AI Agents in Practice

Evolution of GenAI Workflows

Over the past two years, large language models (LLMs) have reshaped the landscape of AI. From simple prompt-based interactions to complex applications across industries, LLMs have evolved rapidly, fueled by breakthroughs in architecture, training techniques, and fine-tuning strategies. As their capabilities evolved, the shift from ChatGPT to today’s agentic systems as of April 2025 marks a natural progression, where the addition of reasoning, planning, and action-taking capabilities represented a major technological leap.

This chapter explores the foundations of LLMs, how they’re built and consumed, and the differences between pre-trained and fine-tuned models. Most importantly, it sets the stage for the next leap forward: the emergence of AI agents.

In this chapter, we will cover the following topics:

  • Understanding foundation models and the rise of LLMs
  • Latest significant breakthroughs
  • Road to AI agents
  • ...

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Over the past two years, large language models (LLMs) have reshaped the landscape of AI. From simple prompt-based interactions to complex applications across industries, LLMs have evolved rapidly, fueled by breakthroughs in architecture, training techniques, and fine-tuning strategies. As their capabilities evolved, the shift from ChatGPT to today’s agentic systems as of April 2025 marks a natural progression, where the addition of reasoning, planning, and action-taking capabilities represented a major technological leap.This chapter explores the foundations of LLMs, how they’re built and consumed, and the differences between pre-trained and fine-tuned models. Most importantly, it sets the stage for the next leap forward: the emergence of AI agents.In this chapter, we will cover the following topics:

  • Understanding foundation models and the rise of LLMs
  • Latest significant breakthroughs
  • Road to AI agents
  • The need...

Technical requirements

You can access the complete code for this chapter in the book’s accompanying GitHub repository at https://github.com/PacktPublishing/AI-Agents-in-Practice.

Understanding foundation models and the rise of LLMs

AI has undergone a fundamental transformation thanks to the emergence of foundation models—versatile, general-purpose models that can be adapted across a wide range of tasks. Among them, LLMs have taken center stage, redefining how we interact with machines through natural language.

From narrow AI to foundation models

Before the rise of foundation models, the field of AI was dominated by narrow AI—systems built to perform one specific task and nothing else. Each use case required a custom pipeline: a unique dataset, a dedicated model architecture, and a specialized training routine. If you wanted to classify emails as spam or not spam, you'd build a spam filter. If you needed to extract names and places from documents, you'd create a named entity recognizer. Want to summarize a news article? That would mean yet another bespoke model.This fragmented approach had several drawbacks. Models were brittle—performing...

Latest significant breakthroughs

The field of GenAI has experienced rapid advancements over the past few years, with breakthroughs that pushed the boundaries of efficiency, adaptability, and reasoning capabilities. In the following sections, we are going to explore some of the latest techniques that significantly enhance GenAI models’ performance while reducing computational demands.

Small language models and fine-tuning

Small language models (SLMs) are becoming increasingly relevant as organizations seek efficient, cost-effective alternatives to large-scale AI systems.

Definition

SLMs are a streamlined category of GenAI models designed to efficiently process and generate natural language while using fewer computational resources than their larger counterparts. Unlike LLMs, which can have hundreds of billions of parameters, SLMs typically contain only a few million to a few billion parameters.

The reduced size of SLMs allows them to be deployed in environments with limited...

Road to AI agents

The rapid evolution of GenAI has taken us from simple automation to increasingly intelligent systems capable of reasoning, learning, and decision-making. In recent years, LLMs have revolutionized how we interact with the surrounding ecosystem, enabling more natural conversations and sophisticated problem-solving.Let’s explore the greatest milestones that have eventually fueled the rise of AI agents.

Text generation

Since the launch of ChatGPT in November 2022, the very first use case that users embraced was that of conversational text generation, such as the following:

  • “Generate a beginner-level description of the nuclear fission”
  • “Draft an email to send to the C-level board of my customer to invite them to our event”
  • “List 10 ideas for an article about AI”
  • “Generate an essay about the French Revolution I have to hand over by tomorrow”

Do any of the preceding scenarios look familiar to you?The text generation...

The need for an additional layer of intelligence: introducing AI agents

LLMs have demonstrated impressive capabilities in generating coherent text, answering questions, and even performing limited problem-solving. However, their fundamental design presents several limitations when it comes to real-world applications:

  • Lack of long-term memory: Most LLMs operate within a fixed context window, meaning they forget previous interactions once the context limit is exceeded. This makes them incapable of learning from past experiences or maintaining continuity over time.
  • No persistent goals or autonomy: LLMs respond to individual prompts but do not operate with a persistent goal-oriented mindset. They cannot proactively make decisions, self-correct, or refine their approach over time.
  • Limited reasoning and multi-step execution: While LLMs can follow instructions within a single prompt, they struggle with executing multi-step workflows, handling complex decision-making, and maintaining logical...

Summary

Over the past two years, AI has undergone a profound transformation, shifting from simple LLM API calls to more sophisticated, interactive, and autonomous systems. The rapid evolution of LLMs has been marked by innovations such as RAG, fine-tuning advancements, and reasoning-focused architectures, all aimed at improving efficiency, adaptability, and cost-effectiveness.Despite these advances, LLMs alone are not enough to meet the growing demand for AI systems that can operate autonomously, make decisions, and interact meaningfully with their environments.This shift marks a pivotal moment in AI development, where the focus is no longer just on making models bigger but on making them smarter. Instead of treating AI as a passive tool that responds to isolated prompts, we are now designing agentic systems that can act, learn, and adapt to complex real-world tasks.In the next chapter, we will examine the emergence of AI agents, their primary components, and the various forms they may...

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Key benefits

  • Build production-ready AI agents with hands-on tutorials for diverse industry applications
  • Explore multi-agent system architectures with practical frameworks for orchestrator comparison
  • Future-proof your AI development with ethical implementation strategies and security patterns
  • Purchase of the print or Kindle book includes a free PDF eBook

Description

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.

Who is this book for?

This book is ideal for AI engineers and data scientists looking to move beyond basic LLM implementations to build sophisticated autonomous agents. Software developers and system architects will find practical guidelines for integrating agents into existing tech stacks. Product managers and technical entrepreneurs will gain strategic insights into how AI agents can solve business problems across industries. A basic understanding of machine learning concepts and working knowledge of Python are required to make the most of this book and implement production-ready AI agent systems.

What you will learn

  • Build core agent components such as LLMs, memory systems, tool integration, and context management
  • Develop production-ready AI agents using frameworks such as LangChain with code
  • Create effective multi-agent systems using orchestration patterns for problem-solving
  • Implement industry-specific agents for e-commerce, customer support, and more
  • Design robust memory architectures for agents with short- and long-term recall
  • Apply responsible AI practices with monitoring, guardrails, and human oversight
  • Optimize AI agent performance and cost for production environments
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Length: 282 pages
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Table of Contents

14 Chapters
Part 1: Foundations of AI Workflows and the Rise of AI Agents Chevron down icon Chevron up icon
Evolution of GenAI Workflows Chevron down icon Chevron up icon
The Rise of AI Agents Chevron down icon Chevron up icon
Part 2: Designing, Building, and Scaling AI Agents Chevron down icon Chevron up icon
The Need for an AI Orchestrator Chevron down icon Chevron up icon
The Need for Memory and Context Management Chevron down icon Chevron up icon
The Need for Tools and External Integrations Chevron down icon Chevron up icon
Building Your First AI Agent with LangChain Chevron down icon Chevron up icon
Multi-Agent Applications Chevron down icon Chevron up icon
Part 3: Road to an Open, Agentic Ecosystem Chevron down icon Chevron up icon
Orchestrating Intelligence: Blueprint for Next-Gen Agent Protocols Chevron down icon Chevron up icon
Navigating Ethical Challenges in Real-World AI Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

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Best book on how to use agents and deploying at scale.
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