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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

Summary

Memory is a foundational component of intelligent AI agents, enabling them to maintain context, personalize interactions, and adapt over time. This chapter explored the spectrum of memory types—short-term and long-term—and their subcategories, including semantic, episodic, and procedural memory.

We examined strategies for managing the limited context window of LLMs, as well as techniques for storing, retrieving, and refreshing memory using both structured and semantic approaches. A hybrid model, combining metadata filtering with vector search, emerged as a powerful method for scalable and relevant memory access.

Finally, we introduced key tools—LangMem, Mem0, and MemGPT—that operationalize memory in different ways, from workflow-aware storage to operating system-inspired context management.

In the next chapter, we will see how to integrate memory with the actual capability of AI agents of “doing things,” by properly defining...

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AI Agents in Practice
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