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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

Different types of memory

Just like humans rely on memory to make sense of the world, AI agents need memory to operate intelligently over time. Memory enables agents to retain information across interactions, remember past events, store useful knowledge, and build consistent behavior. Without memory, even the most powerful language models are stateless—reacting only to the current input with no awareness of what came before.

As AI agents become more sophisticated, so do the demands placed on their memory systems. It’s no longer enough for an agent to simply generate responses—it must remember, adapt, and improve over time. Interestingly, the way researchers design memory architectures for agents draws heavily from how psychologists understand human memory. This parallel has led to a growing taxonomy of memory types in AI, each serving a distinct purpose, from handling recent interactions to building long-term knowledge and skills.

In this section, we&...

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AI Agents in Practice
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