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AI Agents in Practice

You're reading from   AI Agents in Practice Design, implement, and scale autonomous AI systems for production

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Product type Paperback
Published in Aug 2025
Publisher Packt
ISBN-13 9781805801351
Length 282 pages
Edition 1st Edition
Languages
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Author (1):
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Valentina Alto Valentina Alto
Author Profile Icon Valentina Alto
Valentina Alto
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Table of Contents (15) Chapters Close

Preface 1. Part 1: Foundations of AI Workflows and the Rise of AI Agents
2. Evolution of GenAI Workflows FREE CHAPTER 3. The Rise of AI Agents 4. Part 2: Designing, Building, and Scaling AI Agents
5. The Need for an AI Orchestrator 6. The Need for Memory and Context Management 7. The Need for Tools and External Integrations 8. Building Your First AI Agent with LangChain 9. Multi-Agent Applications 10. Part 3: Road to an Open, Agentic Ecosystem
11. Orchestrating Intelligence: Blueprint for Next-Gen Agent Protocols 12. Navigating Ethical Challenges in Real-World AI 13. Other Books You May Enjoy 14. Index

Different types of AI agents

AI agents come in varying levels of complexity and capability, ranging from simple retrieval-based agents to fully autonomous systems. Understanding these different types helps organizations and developers select the right kind of AI agent for specific use cases. In this section, we are going to cluster AI agents into three primary types: retrieval agents, task agents, and autonomous agents.

Retrieval agents

In Chapter 1, we introduced the concept of RAG as a technique in GenAI applications where an LLM retrieves relevant documents or snippets from a knowledge base (properly embedded and stored in a VectorDB) before generating responses.Retrieval AI agents build upon the foundations of RAG but incorporate advanced agentic behaviors, making them more autonomous and adaptive. In fact, we are adding to a standard RAG pipeline an additional layer of intelligence and planning that allows the agent to “strategize” on how to retrieve the most relevant...

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