New Paper Proposes AI Assurance Strategy for Enterprise Systems

A new paper titled 'AI Assurance: A Comprehensive Testing Strategy for Enterprise AI Systems' (arXiv:2605.23459) by authors Chitra Badagi, Divye Singh, Animesh Sen, and Adinath Shirsath tackles the unique challenges of testing modern enterprise AI. The authors argue that traditional software quality assurance is ill-suited for systems built on large language models, retrieval pipelines, and autonomous agents—systems that are probabilistic, context-sensitive, and emergent. Instead of verifying correctness in the classical sense, the paper advocates for evaluating AI systems with increasing confidence through continuous risk reduction.

The paper introduces two key frameworks: an AI Failure Taxonomy that categorizes failure modes specific to AI, and a revised five-layer AI Assurance Pyramid that structures testing across layers including evaluation-driven development, RAG system testing, model lifecycle management, and governance. It positions evaluation as a core engineering discipline, not an afterthought, and highlights that AI failures can cause organizational impacts fundamentally different from deterministic software failures. Engineering leaders and practitioners are given a philosophically grounded yet operationally deployable strategy to build trustworthy AI systems.