(untitled) (American Data Science Journal for Advanced Computations (ADSJAC), 2026)
Automating enterprise operations offers the promise of substantially increased agility and competitive advantage. However, enterprises are still constrained in their ability to leverage such innovations for many operations beyond routine transaction processing. Security and oversight requirements ordinarily preclude the deployment of advanced AI techniques in data-rich enterprise context. Recently proposed retrieval-augmented AI designs provide a means to address some of these limitations, enabling formal institutions to enforce policy and mitigate risk, even when operations require complex reasoning and data synthesis. Yet no formal framework for retrieval-augmented multi-agent systems exists. Such a framework is necessary to support comprehensive validation and evaluation of emerging systems while also guiding practical implementations. Providing such an underpinning constitutes the principal contribution of this work. The contributions of this study formalize the design of retrieval-augmented multi-agent systems. A foundation architectural model is defined, linking agent modularity, open interfaces, and scalable operation to retrieval-augmented operation. Grounding the architecture for enterprise contexts supports a comprehensive theory of retrieval-augmented multi-agent systems. Security and governed operation are addressed via consideration of (1) agent role definitions; (2) data access and provenance management; (3) strategic behaviour, information source maintenance, and understandable queries. Together, these aspects provide the procedural scaffolding necessary for scientifically-grounded operational and performance evaluations, endorsing the continued development of novel retrieval-augmented multi-agent systems for enterprise automation.