The rapid expansion of autonomous AI agents capable of executing multi-step tasks has highlighted the need for rigorous reliability assessment. While benchmark suites such as SWE-bench, GAIA, and OSWorld provide preliminary success metrics, they lack a unified framework for characterizing failure modes across heterogeneous agent architectures. This article addresses this gap by presenting a sys...
The increasing adoption of cryptocurrency has facilitated the emergence of shadow economies that operate beyond traditional regulatory frameworks. Detecting illicit flow patterns within blockchain transactions is critical for financial compliance and risk assessment. This article addresses the following research questions: (RQ1) How can on-chain analytics be leveraged to identify shadow economy...
Designing reliable AI agents requires precise specification of behavioral expectations. This article investigates how Design‑by‑Contract (DbC) principles can be adapted to formally express preconditions, postconditions, and invariants that remain robust across model updates and prompt drift. We outline a pattern repertoire for encoding contracts in a machine‑readable format, and demonstrate how...
Retrieval-Augmented Generation (RAG) systems combine large language models with external knowledge sources to mitigate hallucination and improve factual accuracy. However, the economic cost of RAG—particularly when scaling vector databases versus sparse retrieval pipelines—remains insufficiently characterized. This article investigates the cost-performance trade‑offs of two dominant RAG back‑en...
The rapid adoption of artificial intelligence (AI) technologies in knowledge-intensive industries has sparked debate about its impact on compensation structures and wage inequality. This article investigates whether AI tools increase or decrease salary differentials across skill levels and firm sizes, focusing on empirical evidence from firms with high AI penetration. We pose three research que...
Accurate and efficient sanctions screening is a critical compliance requirement for financial institutions operating across the European Union and United States. While rule‑based systems dominate current workflows, recent advances in neural representation l[REDACTED]g offer the prospect of dramatically reducing false‑positive rates and operational costs. This article investigates the deployment...
The diffusion of generative AI copilots such as Microsoft Copilot and OpenAI ChatGPT has produced a new class of unsanctioned employee tool usage—commonly labeled shadow AI. This article provides a systematic quantification of shadow AI adoption across Fortune 500 enterprises and evaluates its net effect on the organizational capability gap. Employing a mixed‑methods design that integrates a la...
The rapid proliferation of AI‑generated text in scholarly and professional contexts demands robust, multidimensional evaluation tools that go beyond traditional readability formulas such as Flesch‑Kincaid. This article introduces a composite quality metric that integrates four independent dimensions: (1) surface‑level readability, (2) conceptual density, (3) argumentative coherence, and (4) exp...
Explainability in AI-driven predictive maintenance remains a critical but under‑quantified factor in industrial deployments. This article investigates how the reliability and accuracy of AI-generated explanations affect maintenance decision outcomes in large‑scale manufacturing environments. We define explanation quality along three dimensions—clarity, fidelity, and actionable insight—and const...
Supply chain attacks targeting machine l[REDACTED]g (ML) pipelines have emerged as a critical threat vector, compromising model integrity through poisoning, backdoor insertion, and Trojan triggers embedded in ostensibly benign training data. This article systematically reviews the state of the art in model poisoning attack vectors within open-source ML ecosystems, focusing on Hugging Face Hub a...