arXiv:2605.23459v1 Announce Type: cross 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 reduc
The rapid deployment of enterprise AI systems built on large language models and autonomous agents is creating an urgent need for new assurance frameworks beyond traditional software QA.
The lack of robust AI assurance strategies poses significant risks for enterprises deploying complex AI, potentially undermining trust and limiting adoption.
Traditional software quality assurance paradigms are insufficient for AI systems, necessitating a shift towards continuous risk reduction and probabilistic evaluation.
- · AI assurance providers
- · Enterprises adopting comprehensive AI testing
- · Regulatory bodies developing AI safety standards
- · Traditional software QA vendors
- · Enterprises deploying unverified AI systems
- · Developers ignoring AI risk management
Enterprises will invest heavily in new tools and methodologies for AI assurance.
A new industry segment for AI risk management and testing services will emerge and grow significantly.
Robust assurance frameworks will accelerate enterprise AI adoption, leading to further integration of AI agents into core business processes.
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Read at arXiv cs.AI