The field of Generative AI is evolving rapidly. As we move into 2026, the industry’s focus is shifting from foundational model research toward the engineering of robust, scalable, and agent-driven applications. To stay ahead of the curve, we must deepen their understanding of AI engineering, design patterns, and deployment best practices.
Below is a list of five titles published recently (late 2024 through 2025, depending on edition/format/region). These titles move beyond introductory concepts to focus on the production-grade challenges faced by AI engineers and architects.
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1. Designing Multi-Agent Systems: Principles, Patterns and Implementation for AI Agents
Author: Victor Dibia
One major paradigm shift in Generative AI is the move toward autonomous multi-agent applications where multiple specialized AI agents collaborate to achieve complex goals. This book takes a first-principles approach to designing and implementing reliable agentic systems, with an emphasis on practical patterns for orchestration and the real production concerns that come with agents (like evaluation, observability, and trust).
2. Generative AI Design Patterns
Authors: Valliappa Lakshmanan and Hannes Hapke
Building reliable Generative AI applications requires more than just prompt engineering; it demands robust architectural patterns. This book serves as a structured catalog of over 30 design patterns, providing solutions to common production challenges. It covers critical areas like Retrieval-Augmented Generation (RAG), structured output, reasoning chains, and reliability guardrails. For any professional tasked with moving a GenAI proof-of-concept into a stable, enterprise-ready service, this pattern-based approach is invaluable.
3. AI Engineering: Building Applications with Foundation Models
Author: Chip Huyen
This title addresses the lifecycle of building applications with foundation models, helping define the emerging discipline of AI Engineering. It bridges the gap between the theoretical world of machine learning research and the practical demands of software deployment, covering core topics like evaluation, development workflows, and deployment tradeoffs (including latency and cost). It’s a strong resource for AI Engineers, ML Engineers, and technical product managers.
4. Building Applications with AI Agents
Author: Michael Albada
While “Designing Multi-Agent Systems” focuses on principles, this book is more hands-on and implementation-oriented. A standout feature is its scenario-driven approach implemented using multiple frameworks and tools, including LangGraph, LangChain, and GraphRAG. However, this is not a beginner book.
5. Building Generative AI Services with FastAPI: A Practical Approach to Developing Context-Rich Generative AI Applications
Author: Alireza Parandeh
Building a scalable, performant backend service is one the most demanding steps in the GenAI pipeline. This book is a practical, code-focused guide showing how to use FastAPI to build production-grade GenAI services. It tackles critical engineering challenges such as concurrency, streaming (including WebSockets/SSE, depending on implementation), and containerized deployment with Docker, along with reliability and security considerations that matter in real systems.