Overview

DSPy Agentic AI Engineering is a practical guide to building modern AI applications using DSPy, one of the most powerful frameworks for modular and programmable language model systems. This book focuses on helping developers move beyond prompt experimentation and into structured AI engineering with reusable pipelines, optimized workflows, and scalable architectures. Through hands-on examples and real-world development patterns, you'll learn how to build AI agents, retrieval-augmented generation (RAG) systems, tool-calling workflows, and intelligent applications that are reliable, maintainable, and production-ready. The book introduces the core principles behind DSPy programming, including signatures, modules, optimization pipelines, dataset-driven improvements, and evaluation systems. You'll also explore integration strategies for APIs, vector databases, memory systems, and deployment environments used in modern AI infrastructure. What You Will Learn: Core DSPy architecture and modular AI programming Building AI agents and multi-step reasoning systems Retrieval-Augmented Generation (RAG) workflows Tool calling and API integration pipelines Structured outputs and typed generation Prompt optimization and teleprompting systems Dataset engineering and evaluation workflows Memory and context management for AI agents FastAPI, Docker, and production deployment strategies Debugging, monitoring, and improving DSPy applications Whether you're an AI engineer, software developer, or technical builder exploring next-generation LLM systems, this book provides the practical foundation needed to engineer intelligent applications with DSPy.

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