arXiv AI
EnterpriseVal: Quantifying the Efficacy, Reliability and Value of Generative AI in the Enterprise
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arXiv AI
Sep 3
READY or Not: Reliable Enterprise Agent Deployment
READY or Not: Reliable Enterprise Agent Deployment introduces a framework for qualifying AI agents for enterprise workflows. It measures reliability and operating cost under various oversight policies, selects the minimum‑cost policy that meets a specified reliability target, and statistically qualifies it on held‑out cases. In a clinical audit study, READY revealed that two agents with nearly identical autonomous accuracy required markedly different levels of human review to achieve the same reliability target.
arXiv AI
Aug 5
Measurement Without Validity: The Compounding Reliability Problem in Agentic AI Evaluation
arXiv:2608. 00794v2 Announce Type: replace Abstract: Agentic AI evaluation pipelines produce benchmark scores that justify deployment decisions, safety certifications, and regulatory compliance claims.
arXiv AI
Jul 8
EvalLoop: A Methodology for Evaluation-Driven Iterative Improvement of Business AI Systems
arXiv:2607. 05638v1 Announce Type: cross Abstract: Teams deploying large language models in business contexts need evaluation systems, yet most treat evaluation as static model selection: run benchmarks, rank models, deploy the winner.
arXiv AI
Jul 21
Zero Hallucination, by Construction: Hallucination-Aware Layered Oversight for Trustworthy Enterprise AI
arXiv:2607. 17883v1 Announce Type: cross Abstract: Enterprises will not deploy AI agents they cannot trust, and the most-cited reason for distrust is hallucination: confident, fluent output that is simply not true.
arXiv AI
Jun 2
BADGER: Bridging Agentic and Deterministic Evaluation for Generative Enterprise Reasoning
arXiv:2606. 02109v1 Announce Type: new Abstract: Enterprise AI systems that translate natural language into SQL queries and orchestrate multi-step agentic reasoning pipelines require evaluation approaches fundamentally different from academic benchmarks.
arXiv AI
Jul 29
Messier: A High-Resolution Corpus for Cross-Benchmark Agent Evaluation
arXiv:2607. 25891v1 Announce Type: new Abstract: Evaluating AI agents in interactive environments is hindered by fragmented tasks, scaffolds, verifiers, and scoring rules.