Agentic AI Engineering

Build a practical understanding of modern agentic AI systems-from foundational LLM concepts to intelligent agents capable of using tools, retrieving knowledge, coordinating workflows, and operating within controlled production environments.

Agentic AI Engineering explores the architecture and engineering practices behind reliable AI agents. It covers planning, tool integration, memory, retrieval-augmented generation (RAG), Model Context Protocol (MCP), multi-agent coordination, evaluation, security, observability, deployment, and governance.

Designed for developers, software engineers, AI practitioners, and technical learners, this book connects core concepts with engineering workflows, implementation labs, enterprise case studies, and production-readiness reviews.

Inside this book:

• LLM fundamentals and agent architecture

• Planning, reasoning, and tool execution

• Memory systems and retrieval-augmented generation

• MCP and multi-agent workflows

• LangGraph and durable execution concepts

• Evaluation, testing, and observability

• Security, governance, and human approval

• Deployment, reliability, and cost controls

• Practical engineering labs and enterprise case studies

A practical engineering reference for understanding how to design, evaluate, and operate agentic AI applications responsibly.