AI Engineering and Agentic AI: Designing Autonomous Language Model Systems with Memory, Tools, and Safe Deployment
Product ID: 836309499
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Artificial intelligence is no longer limited to research labs, it is now a core component of modern software systems. This book provides a comprehensive, practical guide to building real-world AI applications using foundation models, large language models, and agentic systems. AI Engineering and Agentic AI bridges the gap between theory and production. It focuses on how to design, build, evaluate, and deploy intelligent systems that can reason, act, and interact reliably in real-world environments. Inside this book, you will learn how to: Understand how large language models work and where they succeed and fail Design effective prompts and structured prompt systems for consistent outputs Build retrieval-augmented generation (RAG) systems grounded in real data Develop AI agents with memory, tools, and multi-step reasoning capabilities Architect scalable workflows and multi-agent systems Evaluate AI systems using practical metrics and benchmarks Deploy, monitor, and optimize AI applications in production Manage cost, latency, safety, and compliance risks Apply AI across industries including business, software, healthcare, and education Unlike many resources that focus only on models or theory, this book emphasizes end-to-end system design . It reflects current industry practices, including agent frameworks, evaluation pipelines, and real-world deployment strategies. Whether you are a software engineer, data scientist, technical leader, or an ambitious beginner, this book equips you with the knowledge needed to move from experimentation to production-ready AI systems. Key Features Practical, system-oriented approach to AI engineering Coverage of both prompt engineering and agentic AI Real-world examples and deployment insights Clear explanation of modern tools, frameworks, and architectures Focus on reliability, safety, and scalability Review: The Book That Finally Bridges the Gap Between AI Hype and Actual Engineering - Every week there's a new tutorial on building AI apps, and almost none of them tell you what happens when things break in production at 2am. This book does. What separates it from the crowded field of LLM content is the relentless focus on end-to-end system design — memory architecture, multi-agent workflows, evaluation pipelines, cost and latency tradeoffs — the unglamorous stuff that actually determines whether your AI system survives contact with real users. The RAG system coverage alone is worth the price if you've ever watched a well-prompted model confidently hallucinate its way through a customer query. I came in as a software engineer who'd been experimenting with AI features and left with a completely different mental model of what it means to build these systems responsibly. It doesn't talk down to beginners but it doesn't coddle them either. If you're serious about moving from AI tinkering to production-grade agentic systems, this is the textbook the industry didn't know it needed yet. Review: Building Smarter AI Systems with Confidence - This book is an outstanding guide for anyone interested in the future of AI engineering and agentic systems. Kent Stuber explains complex concepts like prompt engineering, AI agents, memory systems, RAG pipelines, and model deployment in a clear and practical way. What makes this book stand out is how it balances technical depth with real-world applications, making it valuable for both beginners and experienced developers. The discussions on AI safety, ethics, scalability, and autonomous systems are especially relevant in today’s rapidly evolving tech landscape. Every chapter feels purposeful, well-structured, and forward-thinking.
| Best Sellers Rank | #319,255 in Kindle Store ( See Top 100 in Kindle Store ) #144 in Generative AI (Kindle Store) #214 in Computer Software (Kindle Store) #259 in AI & Semantics |
D**R
The Book That Finally Bridges the Gap Between AI Hype and Actual Engineering
Every week there's a new tutorial on building AI apps, and almost none of them tell you what happens when things break in production at 2am. This book does. What separates it from the crowded field of LLM content is the relentless focus on end-to-end system design — memory architecture, multi-agent workflows, evaluation pipelines, cost and latency tradeoffs — the unglamorous stuff that actually determines whether your AI system survives contact with real users. The RAG system coverage alone is worth the price if you've ever watched a well-prompted model confidently hallucinate its way through a customer query. I came in as a software engineer who'd been experimenting with AI features and left with a completely different mental model of what it means to build these systems responsibly. It doesn't talk down to beginners but it doesn't coddle them either. If you're serious about moving from AI tinkering to production-grade agentic systems, this is the textbook the industry didn't know it needed yet.
C**T
Building Smarter AI Systems with Confidence
This book is an outstanding guide for anyone interested in the future of AI engineering and agentic systems. Kent Stuber explains complex concepts like prompt engineering, AI agents, memory systems, RAG pipelines, and model deployment in a clear and practical way. What makes this book stand out is how it balances technical depth with real-world applications, making it valuable for both beginners and experienced developers. The discussions on AI safety, ethics, scalability, and autonomous systems are especially relevant in today’s rapidly evolving tech landscape. Every chapter feels purposeful, well-structured, and forward-thinking.
S**N
AI is the future
The book does a good job of breaking down complex AI concepts into real world applications. I liked that it focuses not just on theory, but on how to build, deploy and manage AI systems. Great book to use to learn about modern AI engineering without getting lost.
E**N
Helpful!
This book was a great guide on building and creating! Even someone that doesn't use AI a lot would understand how to do it.
A**R
From Chatbot to Coworker
AI Engineering and Agentic AI isn't another hype-driven AI book it's the realistic, practical guide engineers have been waiting for. It bridges the gap between playing with ChatGPT and building agents that actually remember, reason, and deploy safely. The chapters on RAG, multi agent systems, and production risks are gold. No fluff, just real architecture. Finally, AI engineering with adult supervision.
K**L
5 stars
Great book super informative highly recommend
R**H
!
This book has a very good concept of how to build modern reliable ai systems correctly.
R**E
AI uniqueness
This is a unique way to use AI. Very interesting book.
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