AI Assurance Strategy for Enterprise Systems
A new paper on arXiv (2605.23459) proposes a comprehensive testing strategy for enterprise AI systems built on large language models, retrieval pipelines, and autonomous agents. It argues that these probabilistic, context-sensitive, and emergent systems cannot be verified as correct in the classical sense but only evaluated with increasing confidence. The strategy focuses on continuous risk reduction rather than strict correctness verification, treats evaluation as a core engineering discipline, and highlights that failures in AI assurance lead to organizational impacts fundamentally different from traditional deterministic software. The paper introduces a structured AI Failure Taxonomy and a revised five-layer AI assurance framework.
Key facts
- arXiv paper 2605.23459 proposes AI assurance strategy for enterprise systems
- Systems are built on LLMs, retrieval pipelines, and autonomous agents
- AI systems are probabilistic, context-sensitive, and emergent
- Testing focuses on continuous risk reduction, not strict correctness
- Evaluation is treated as a core engineering discipline
- Failures cause organizational impacts different from deterministic software
- Introduces a structured AI Failure Taxonomy
- Proposes a revised five-layer AI assurance framework
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- arXiv