Build Real AI Systems, Not Toy Examples
The Agentic AI Engineering Series is a comprehensive three-volume journey into modern AI engineering. Rather than presenting isolated code snippets or simplified demonstrations, this series teaches you how to design, build, deploy, and maintain intelligent systems that solve real-world problems.
Across the series, you will progressively construct production-quality applications while gaining a deep understanding of how autonomous agents reason, retrieve information, remember previous interactions, collaborate with external tools, and make complex decisions. Every concept is introduced through practical implementation instead of abstract theory, allowing you to understand not only how modern AI frameworks work, but also why they work.
A Learning Path Built Around Real Projects
Unlike traditional AI books that rely on disconnected examples, every volume in this series revolves around complete software projects. Each project grows chapter after chapter, gradually introducing new capabilities while reinforcing concepts learned earlier.
You won't simply experiment with APIs. You'll engineer intelligent applications from the ground up, understanding every architectural decision before adopting higher-level frameworks. This approach enables you to confidently build your own systems instead of depending on copy-and-paste tutorials.
From First Principles to Production
The series begins by explaining the core building blocks of intelligent software before introducing modern development tools such as LangChain, Model Context Protocol (MCP), LangGraph, Retrieval-Augmented Generation (RAG), vector databases, memory architectures, tool calling, and multi-agent collaboration.
Every abstraction is carefully explained. You'll understand what happens behind the scenes instead of relying on hidden implementation details.
Every project is built from first principles before modern frameworks are introduced.
What You Will Learn
- Large Language Model fundamentals
- Autonomous AI agents
- Reasoning and planning strategies
- Conversation memory systems
- Retrieval-Augmented Generation (RAG)
- Vector databases
- Tool calling and external integrations
- Model Context Protocol (MCP)
- LangChain and LangGraph
- Multi-agent architectures
- Production deployment
- Testing, debugging, and maintenance
Practice Comes First
Every chapter concludes with practical exercises and production-ready code that you can study, modify, and extend. Instead of reading about AI, you'll actively build working applications that become more capable as your knowledge grows.
The accompanying downloadable resources include complete source code, datasets, configuration files, reusable templates, and project assets designed to accelerate your learning while encouraging experimentation.
Designed for Developers
Whether you're an experienced Python developer exploring AI for the first time or a software engineer looking to transition into modern AI systems, this series provides a structured roadmap from fundamental concepts to advanced engineering practices.
No unnecessary mathematics. No academic detours. No oversized theoretical chapters. Every page focuses on practical engineering decisions that help you build reliable, maintainable, and scalable AI applications.
The Waskey Press Philosophy
At Waskey Press, we believe technical books should teach lasting skills rather than temporary trends. Every title in the Agentic AI Engineering Series emphasizes understanding over memorization, engineering over automation, and craftsmanship over shortcuts.
By the end of this series, you won't simply know how to use today's AI frameworks—you'll understand the engineering principles that will allow you to adapt to tomorrow's technologies with confidence.
