AGENTIC AI ENGINEERING: A Handbook for Designing Autonomous AI Systems with Large Language Models, Tools, Memory, and Multi-Agent Architectures. (ENGINEERING GUIDE SERIES)

Artificial intelligence is entering a new era. The future of computing will not be defined only by systems that answer questions or generate content, but by intelligent systems capable of understanding goals, reasoning through problems, using tools, managing information, and taking meaningful actions. This new generation of technology is known as Agentic AI — the foundation of autonomous intelligent systems.

Agentic AI Engineering provides a comprehensive guide to understanding, designing, and developing AI agents that can operate in complex digital environments. This handbook explores the engineering principles behind autonomous AI systems and explains how large language models, memory systems, tool integration, planning mechanisms, and multi-agent architectures work together to create intelligent applications.

Unlike traditional artificial intelligence systems that mainly analyze information or provide responses, agentic AI systems are designed to pursue objectives, make decisions, interact with software platforms, retrieve knowledge, and complete multi-step tasks with increasing levels of autonomy. Understanding how these systems are built is becoming an essential skill for the next generation of AI engineers, software developers, researchers, and technology professionals.

Inside this handbook, readers will discover:

• The evolution of artificial intelligence from predictive models and generative AI to autonomous agent-based systems.

• How large language models function as the reasoning foundation behind modern AI agents.

• The architecture of intelligent agents, including perception, reasoning, memory, planning, and action components.

• How AI agents use tools, APIs, databases, and external systems to perform real-world tasks.

• The role of short-term and long-term memory in creating adaptive and personalized intelligence.

• How vector databases and retrieval-augmented generation improve agent knowledge and accuracy.

• The principles behind multi-agent systems, collaborative intelligence, and specialized AI teams.

• Software engineering practices for building reliable, scalable, and maintainable AI agent applications.

• Security, safety, ethical design, and human oversight principles required for responsible autonomous systems.

• Real-world applications of agentic AI across business, scientific research, healthcare, engineering, robotics, and smart technologies.

This book also examines the future of autonomous intelligence, including emerging trends such as self-improving AI systems, autonomous software engineering, AI-powered organizations, and the evolving relationship between humans and intelligent machines.

Designed for AI engineers, software developers, computer science students, researchers, technology architects, and professionals preparing for the future of intelligent computing, this handbook provides both foundational knowledge and practical insights into one of the most important technological developments of the modern era.

As artificial intelligence continues to evolve, the ability to design reliable and responsible autonomous systems will become a defining engineering skill. The future will not belong only to those who build smarter AI models, but to those who understand how to transform those models into intelligent systems that can collaborate, adapt, and create real-world value.

Agentic AI Engineering is your guide to understanding and building the next generation of autonomous intelligent systems.