A Generative AI and Retrieval-Augmented Generation Framework for Intelligent, Adaptive

Abstract

Enterprises are adding generative artificial intelligence (AI) to their workflows to classify documents, draft responses, recommend decisions, and handle exceptions. Most discussion focuses on what these capabilities can do. Much less attention goes to the architectural qualities that decide whether they succeed in production: throughput under high volume, adaptability to change, safety of AI outputs, auditability for regulators, reliability when components fail, and modifiability as models and rules evolve. This article takes a software architecture perspective on generative AI and retrieval-augmented generation (RAG) in enterprise workflows. It identifies six quality attributes as the architecture's main drivers and expresses each as a measurable scenario. It then proposes a reference architecture that combines an event streaming backbone, a configurable workflow engine, RAG-based AI services, a guardrail gateway, and an audit-ready compliance ledger. The article maps architectural tactics to each quality attribute and records the key design decisions with their alternatives and rationale. Using a design-oriented approach grounded in ten studies published between 2017 and 2024, it maps the evidence base and traces an event through the architecture. It illustrates the architecture with a payments company handling merchant chargeback disputes at high volume. It argues that intelligent, adaptive, and automated workflows are achieved by designing explicitly for quality attributes, not by adding AI capabilities alone.