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AI Agent Engineering: Build Intelligent Autonomous Systems with LLMs, Tool Use, Memory, RAG, MCP, and Multi-Agent Architectures
Paperback
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ISBN13: 9798176114188
Publisher: Independently Published
Published: Sep 21 2026
Pages: 354
Weight: 1.24
Height: 0.74 Width: 6.69 Depth: 9.61
Language: English
Publisher: Independently Published
Published: Sep 21 2026
Pages: 354
Weight: 1.24
Height: 0.74 Width: 6.69 Depth: 9.61
Language: English
AI Agent Engineering: Build Intelligent Autonomous Systems with LLMs, Tool Use, Memory, RAG, MCP, and Multi-Agent Architectures What if your AI could do more than answer questions? What if it could reason through problems, use external tools, retrieve information, remember context, interact with APIs, and collaborate with other AI agents to accomplish complex tasks? That is the world of AI agent engineering. AI Agent Engineering is a practical guide to understanding and building intelligent autonomous systems powered by large language models (LLMs). It takes you beyond basic prompting and chatbots into the architecture, technologies, and engineering principles behind modern AI agents. Inside this book, you will explore: - LLM-powered agents and how they reason, plan, and make decisions
- Tool use and function calling for connecting agents to APIs, databases, applications, and external systems
- Memory architectures for maintaining useful context across interactions
- Retrieval-Augmented Generation (RAG) for connecting agents to external knowledge
- Model Context Protocol (MCP) for integrating AI models with tools, data, and services
- Planning and orchestration for handling complex multi-step tasks
- Multi-agent architectures for creating systems where specialized agents collaborate
- Context and state management for building more reliable agent workflows
- Evaluation, observability, and reliability for moving beyond prototypes
- Security considerations for protecting tools, data, credentials, and agent interactions You will also develop a clearer understanding of how these components fit together to create practical agentic systems-and the engineering challenges that emerge when AI moves from simply generating responses to taking actions. Why Read This Book? The AI industry is rapidly moving from applications that generate text and images toward systems capable of reasoning, retrieving information, using tools, executing workflows, and working autonomously. This creates a growing need for engineers and builders who understand not only how LLMs work, but how to build reliable systems around them. Whether you are a software developer, AI engineer, technical founder, automation builder, student, or technology professional, this book can help you develop the knowledge needed to navigate the rapidly evolving world of agentic AI. What You Can Gain By exploring the concepts in this book, you can: - Understand the architecture behind modern AI agents
- Design applications that combine LLMs with tools and external data
- Build more context-aware and knowledge-grounded AI systems
- Understand when and how to use memory, RAG, MCP, and multi-agent patterns
- Recognize common challenges in autonomous AI workflows
- Develop a stronger foundation for building and evaluating agentic applications The transition from AI that responds to AI that acts is already underway. The tools, architectures, and techniques used to build these systems are evolving quickly. Learning the foundations now can help you understand where the technology is heading and prepare you to build with it. Don't just use AI agents. Learn how to engineer them. If you're ready to move beyond basic LLM applications and explore the architecture behind intelligent, autonomous, tool-using AI systems, AI Agent Engineering is your starting point. Turn intelligent models into intelligent systems. Start reading today.
- Memory architectures for maintaining useful context across interactions
- Retrieval-Augmented Generation (RAG) for connecting agents to external knowledge
- Model Context Protocol (MCP) for integrating AI models with tools, data, and services
- Planning and orchestration for handling complex multi-step tasks
- Multi-agent architectures for creating systems where specialized agents collaborate
- Context and state management for building more reliable agent workflows
- Evaluation, observability, and reliability for moving beyond prototypes
- Security considerations for protecting tools, data, credentials, and agent interactions You will also develop a clearer understanding of how these components fit together to create practical agentic systems-and the engineering challenges that emerge when AI moves from simply generating responses to taking actions. Why Read This Book? The AI industry is rapidly moving from applications that generate text and images toward systems capable of reasoning, retrieving information, using tools, executing workflows, and working autonomously. This creates a growing need for engineers and builders who understand not only how LLMs work, but how to build reliable systems around them. Whether you are a software developer, AI engineer, technical founder, automation builder, student, or technology professional, this book can help you develop the knowledge needed to navigate the rapidly evolving world of agentic AI. What You Can Gain By exploring the concepts in this book, you can: - Understand the architecture behind modern AI agents
- Design applications that combine LLMs with tools and external data
- Build more context-aware and knowledge-grounded AI systems
- Understand when and how to use memory, RAG, MCP, and multi-agent patterns
- Recognize common challenges in autonomous AI workflows
- Develop a stronger foundation for building and evaluating agentic applications The transition from AI that responds to AI that acts is already underway. The tools, architectures, and techniques used to build these systems are evolving quickly. Learning the foundations now can help you understand where the technology is heading and prepare you to build with it. Don't just use AI agents. Learn how to engineer them. If you're ready to move beyond basic LLM applications and explore the architecture behind intelligent, autonomous, tool-using AI systems, AI Agent Engineering is your starting point. Turn intelligent models into intelligent systems. Start reading today.
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