Mastering Ai Agents A Practical Handbook For Understanding Building And Leveraging Llm Powered Autonomous Systems To Automate Tasks Solve Complex Problems And Lead The Ai Revolution Pdf
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Building AI Agents with LLMs, RAG, and Knowledge Graphs
Author: Salvatore Raieli
language: en
Publisher: Packt Publishing Ltd
Release Date: 2025-07-11
Master LLM fundamentals to advanced techniques like RAG, reinforcement learning, and knowledge graphs to build, deploy, and scale intelligent AI agents that reason, retrieve, and act autonomously Key Features Implement RAG and knowledge graphs for advanced problem-solving Leverage innovative approaches like LangChain to create real-world intelligent systems Integrate large language models, graph databases, and tool use for next-gen AI solutions Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThis AI agents book addresses the challenge of building AI that not only generates text but also grounds its responses in real data and takes action. Authored by AI specialists with deep expertise in drug discovery and systems optimization, this guide empowers you to leverage retrieval-augmented generation (RAG), knowledge graphs, and agent-based architectures to engineer truly intelligent behavior. By combining large language models (LLMs) with up-to-date information retrieval and structured knowledge, you'll create AI agents capable of deeper reasoning and more reliable problem-solving. Inside, you'll find a practical roadmap from concept to implementation. You’ll discover how to connect language models with external data via RAG pipelines for increasing factual accuracy and incorporate knowledge graphs for context-rich reasoning. The chapters will help you build and orchestrate autonomous agents that combine planning, tool use, and knowledge retrieval to achieve complex goals. Concrete Python examples built on popular libraries, along with real-world case studies, reinforce each concept and show you how these techniques come together. By the end of this book, you’ll be well-equipped to build intelligent AI agents that reason, retrieve, and interact dynamically, empowering you to deploy powerful AI solutions across industries.What you will learn Learn how LLMs work, their structure, uses, and limits, and design RAG pipelines to link them to external data Build and query knowledge graphs for structured context and factual grounding Develop AI agents that plan, reason, and use tools to complete tasks Integrate LLMs with external APIs and databases to incorporate live data Apply techniques to minimize hallucinations and ensure accurate outputs Orchestrate multiple agents to solve complex, multi-step problems Optimize prompts, memory, and context handling for long-running tasks Deploy and monitor AI agents in production environments Who this book is for If you are a data scientist or researcher who wants to learn how to create and deploy an AI agent to solve limitless tasks, this book is for you. To get the most out of this book, you should have basic knowledge of Python and Gen AI. This book is also excellent for experienced data scientists who want to explore state-of-the-art developments in LLM and LLM-based applications.
AI Agents in Practice
Author: Valentina Alto
language: en
Publisher: Packt Publishing Ltd
Release Date: 2025-08-28
Master the art of building AI agents with this hands-on guide to orchestration, multi-agent systems, real-world case studies, and ethical insights to drive immediate business impact Key Features 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 Book DescriptionAs 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.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 Who this book is 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.
Using N8n to Develop Multi-Agent AI Systems
Author: Darryl Jeffery
language: en
Publisher: Independently Published
Release Date: 2025-04-06
Book Title: Using n8n to Develop Multi-Agent AI Systems: Enabling Scalable Orchestration, Real-Time Integration, and Automated Intelligence for Smarter Agent Collaboration Book Description: Unlock the full potential of AI-powered automation with this definitive guide to building intelligent, scalable, and collaborative multi-agent systems using n8n. Whether you're a developer, technical lead, or innovation strategist, this hands-on book gives you step-by-step guidance to design, implement, and scale autonomous AI agents that solve complex problems and optimize real-world workflows. With practical examples, expert strategies, and deep insights, you'll learn how to connect Large Language Models (LLMs) with real-time data sources, integrate APIs, and orchestrate intelligent behavior across decentralized agents using the powerful capabilities of n8n's visual workflow platform. From foundational concepts to advanced architectures like Retrieval-Augmented Generation (RAG), this book delivers a complete system for building next-generation AI solutions-combining cognitive frameworks, automation design, and seamless interoperability. Inside, you'll learn how to: Build and deploy intelligent AI agents with n8n Architect multi-agent systems using RAG for dynamic reasoning and collaboration Design real-time workflows that integrate with APIs, databases, and cloud services Optimize business processes, scale automation, and achieve competitive advantage Create autonomous, LLM-powered systems capable of decision-making and task execution Leverage n8n to automate repetitive operations and unlock innovation at scale Apply best practices for agent communication, performance, monitoring, and error handling Future-proof your applications with modern agentic AI architectures Whether you're automating enterprise workflows, building AI-native applications, or exploring LLM-based agent collaboration, this book provides the practical knowledge and frameworks to succeed. Grounded in real-world case studies and packed with reusable patterns, it's a must-read for anyone serious about mastering multi-agent AI with n8n. Make smarter decisions, build smarter systems, and lead the AI automation revolution-one agent at a time.