Architecting the Next-Gen AI. A System Design Framework for Reliable Retrieval-Augmented Generation (RAG) and Agentic Workflow Deployment - Clinton S. Dunavant

Building an AI demo is easy. Building an AI system that stays reliable when retrieval is noisy, tools fail, context is incomplete, and users expect consistent answers is where most teams struggle. Too many projects promise intelligent automation, yet collapse under weak architecture, poor observability, brittle prompts, and unreliable workflow design.

Architecting the Next-Gen AI addresses that problem head-on. This book presents a practical system design framework for building production-ready AI applications powered by Retrieval-Augmented Generation, agentic workflows, structured orchestration, and dependable deployment patterns. Grounded in the manuscript's emphasis on architecture, boundaries, determinism, retrieval quality, and operational trust, it treats the language model as one component inside a larger, controlled system rather than the entire application.

Inside, readers will learn how to design scalable RAG pipelines, structure context for stronger response quality, deploy agentic systems with clear guardrails, integrate tools safely, manage orchestration across workflows, and improve reliability through evaluation, telemetry, security, and cost-aware performance tuning. The result is a clearer path from prototype to production for engineers, architects, and technical leaders who need AI systems that do more than impress in a demo.

Whether the goal is to build enterprise AI platforms, improve LLM system design, strengthen AI architecture, or deploy reliable agent workflows that teams can actually trust, this book offers a focused blueprint for doing the work well.