🤖 AI Summary
This work addresses the challenges posed by enterprise AI systems—increasingly characterized by probabilistic behavior, context sensitivity, and emergent properties due to large language models, retrieval-augmented generation (RAG), and autonomous agents—which render traditional software quality assurance methods inadequate for managing novel risks. To tackle this, the paper proposes an AI assurance framework centered on continuous risk reduction, introducing a structured taxonomy of AI failures and redefining the assurance pyramid across five layers: data, model, system, application, and organization. The framework deeply integrates evaluation throughout the development lifecycle and emphasizes the distinct organizational impacts of AI failures, fostering an assessment-driven engineering culture. It offers engineering leaders a theoretically grounded yet practically actionable strategy to significantly enhance the trustworthiness assessment and governance of enterprise AI systems.
📝 Abstract
Enterprise AI systems, built on large language models, retrieval pipelines and autonomous agents, introduce a class of risks that traditional software quality assurance was never designed to address. These systems are probabilistic, context-sensitive and emergent: they cannot be verified to be correct in the classical sense, but only evaluated with increasing confidence. This paper presents a comprehensive assurance strategy for enterprise AI systems built around three key principles: first, that AI testing should focus on continuous risk reduction rather than strict correctness verification; second, that evaluation must be treated as a core engineering discipline alongside development; and third, that failures in AI assurance can lead to organizational impacts that are fundamentally different from those seen in traditional deterministic software systems. We introduce a structured AI Failure Taxonomy, propose a revised five-layer AI Assurance Pyramid and provide operational guidance on evaluation-driven development, RAG system testing, model lifecycle management and governance. The goal is to equip engineering leaders and practitioners with a strategy that is both philosophically grounded and operationally deployable.