AI Engineering and Agentic AI: Designing Autonomous Language Model Systems with Memory, Tools, and Safe Deployment

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