Building the AI Engine: A Hands-On Guide to Building Scalable Infrastructure, Data Pipelines, and Model Lifecycle Systems for Generative and Agentic A (Paperback) (ISBN-13: 9798249720629)

AI is no longer just about models that generate text or predictions.

The real transformation is happening at the system level.

Today's most powerful AI solutions are not built around a single model - they are built as coordinated networks of intelligent agents that can reason, retrieve knowledge, make decisions, and execute tasks together.

Welcome to the world of Multi-Agent AI Systems.

Multi-Agent AI System Design is a practical guide for engineers, architects, builders, and forward-thinking professionals who want to move beyond simple GenAI demos and start building AI systems that actually work in production.

This book shows you how to design intelligent systems where:

• AI agents collaborate instead of operating in isolation
• Knowledge is retrieved dynamically using RAG
• Decisions are structured through workflows
• Tasks are executed through tools
• Systems remain scalable, observable, and enterprise-ready

Instead of focusing on theory alone, this book takes a hands-on architectural approach - breaking down how modern AI platforms are built using coordinated agents powered by large language models.

You'll learn how to structure AI systems that can:

• Plan tasks
• Delegate responsibilities
• Retrieve and reason over data
• Execute actions
• Monitor outcomes
• Improve continuously

This book is written for:

• AI Engineers looking to build production-grade agent systems
• Software Developers integrating GenAI into real applications
• Solution Architects designing intelligent enterprise platforms
• Data & ML Engineers working with RAG pipelines
• Tech Leaders exploring scalable AI automation
• Startups building AI-native products
• Anyone ready to move from "AI prompts" to "AI systems"

If you already understand the potential of AI but want to learn how to build structured, reliable, and scalable solutions - this book is for you.

• Design multi-agent architectures from the ground up
• Build collaborative agent workflows
• Integrate Retrieval-Augmented Generation (RAG)
• Create tool-using and memory-aware agents
• Implement planning and decision systems
• Apply LLMOps principles for stability and scale
• Design observable, governable AI systems for enterprise use

AI is evolving from assistance to execution.

The future belongs to systems that don't just respond - but coordinate, reason, and act.

Multi-Agent AI System Design gives you the blueprint to build them.