Enterprise RAG Architecture: Cloud-Native Deployment Patterns, Governance Frameworks, and Operational Leadership Models for Large-Scale Intelligence Agent Programs
Year: 2026
Type: article
Abstract: Retrieval-augmented generation (RAG) has become the dominant architectural approach for deploying large language model-based intelligence agents inside enterprise environments, combining parametric model reasoning [1] with grounded retrieval from an organization's own knowledge stores [2]. Published research on RAG largely treats retrieval quality and generation accuracy as isolated benchmarking problems [3], leaving a gap in how RAG-based agents should actually be architected and governed once they move from a pilot into a production service a delivery organization is held accountable for. This paper addresses that gap through two contributions: a five-layer cloud-native reference architecture for enterprise RAG deployment, and an Operational Leadership Model defining the governance responsibilities Head of Engineering and Head of Service Delivery roles must exercise once a RAG-based agent program is live and client-facing. Drawing on direct experience architecting and governing production Microsoft Copilot Studio agent programs across multiple concurrent enterprise engagements, we argue that a meaningful share of enterprise RAG failures are governance failures rather than retrieval or generation failures, and that the existing literature's near-exclusive focus on model-level metrics leaves delivery leaders without a usable operating model of their own. The reference architecture and leadership model are evaluated against qualitative deployment experience, showing that organizations pairing architectural discipline with explicit leadership accountability see meaningfully more stable service delivery outcomes than organizations treating RAG as a purely technical initiative. This work gives cloud architects and delivery leaders a shared, actionable model for governing enterprise RAG programs that neither group has been able to draw on from the literature alone.
Author: Prakash Nagaraj
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7222384475