arXiv AI
EnterpriseVal: Quantifying the Efficacy, Reliability and Value of Generative AI in the Enterprise
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arXiv AI
Oct 2
Continuous Process-Level Evaluation for Evolving Enterprise AI Agent Skills
The paper introduces a continuous evaluation framework that assesses both outcome-level and process-level aspects of evolving enterprise AI agent skills. It applies this framework to two variants of a Business Value Determination skill, running 240 trials across multiple models, harnesses, and specifications. The results show that while most trials pass final numerical checks, a large majority still exhibit process-level deviations, and dependency attribution reduces the number of failed checks per run. The framework also provides reusable regression tests and highlights specification sensitivity across configurations.
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
Oct 15
AgentStripes: An Evidence-Graded Maturity Model for AI Agents
arXiv:2610.18603v1 Announce Type: new Abstract: Benchmarks report what an Artificial Intelligence (AI) agent can do on a task suite, but not whether one released version has been engineered with the...
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.