Abstract The rapid proliferation of artificial intelligence has created a complex ecosystem of products, platforms, and organizations, yet empirical understanding of its structural characteristics remains limited. This study empirically maps the observed AI product ecosystem by examining 3,027 product records, focusing on market concentration, organizational diversity, startup-product relationships, and pricing metadata completeness. The analysis reveals a dual structural pattern. Within the subset of products with... The rapid proliferation of artificial intelligence has created a complex ecosystem of products, platforms, and organizations, yet empirical understanding of its structural characteristics remains limited. This study empirically maps the observed AI product ecosystem by examining 3,027 product records, focusing on market concentration, organizational diversity, startup-product relationships, and pricing metadata completeness. The analysis reveals a dual structural pattern. Within the subset of products with identifiable organizational relationships (42 products across 17 startups), product representation is highly concentrated: four companies account for 57.14% of linked products, with a Herfindahl Hirschman Index of 1,281 and a Gini coefficient of 0.434. However, the ecosystem simultaneously demonstrates organizational diversity, with a Shannon diversity index of 2.450 and Simpson diversity measure of 0.872. Critically, the study identifies substantial metadata limitations: only 1.39% of product records contain identifiable company associations, while 97.72% lack explicit pricing classifications. These findings demonstrate that ecosystem structure cannot be assessed independently of data quality observed concentration among major companies coexists with broad organizational participation, but incomplete entity linkage and pricing metadata severely constrain inference. The study contributes methodologically by distinguishing between substantive ecosystem characteristics and data quality artifacts, and empirically by characterizing the AI product ecosystem as organizationally diverse yet product concentrated. Results highlight the need for improved entity resolution, standardized pricing taxonomies, and multi source data integration to support robust future analyses of the evolving AI product landscape. Read More Read Less

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Abstract As large language model (LLM) based applications evolve into autonomous, action oriented AI agents, their evaluation can no longer rely on binary success/failure metrics. Agentic systems operate through dynamic, multi-step interactions with tools and environments, introducing complex failure modes that propagate and cascade. This study presents a hierarchical failure taxonomy and an empirical reliability assessment using the Agent Failure Atlas 2026 benchmark. Analyzing 3,336... As large language model (LLM) based applications evolve into autonomous, action oriented AI agents, their evaluation can no longer rely on binary success/failure metrics. Agentic systems operate through dynamic, multi-step interactions with tools and environments, introducing complex failure modes that propagate and cascade. This study presents a hierarchical failure taxonomy and an empirical reliability assessment using the Agent Failure Atlas 2026 benchmark. Analyzing 3,336 agent runs encompassing 43,477 trajectory steps and 3,802 failure events, we characterize agent reliability across four dimensions: frequency, criticality, cascading behavior, and recoverability. Our results reveal a two dimensional reliability landscape: high prevalence but largely recoverable failures (e.g., tool use and state tracking errors) contrast sharply with low prevalence but highly critical failures (e.g., validation and output errors). Furthermore, we show that intermediate failures often resolve through agent self correction, decoupling run level error rates from final task success. By shifting the analytical focus from isolated outcome accuracy to mechanistic failure propagation, this framework provides a foundational methodology for reliability engineering, risk mitigation, and the safe deployment of autonomous AI systems. Read More Read Less

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Abstract Integrating Artificial Intelligence (AI) into language assessment offers transformative opportunities for scalability and personalised feedback, but it also raises critical concerns about validity, equity, privacy, and pedagogical value. This study evaluates the responsible deployment of AI-generated language assessments using the Responsible AI Language Education Benchmark, an analysis-ready dataset comprising 960 learner profiles across eight countries and four CEFR proficiency levels. We assessed three anonymized... Integrating Artificial Intelligence (AI) into language assessment offers transformative opportunities for scalability and personalised feedback, but it also raises critical concerns about validity, equity, privacy, and pedagogical value. This study evaluates the responsible deployment of AI-generated language assessments using the Responsible AI Language Education Benchmark, an analysis-ready dataset comprising 960 learner profiles across eight countries and four CEFR proficiency levels. We assessed three anonymized generative AI models (Model_A, Model_B, and Model_C) using a multidimensional, non compensatory framework requiring a minimum threshold score of 70 across four pillars: Validity, Equity, Privacy, and Pedagogical Value. Results indicate that Model_A provides the most balanced profile, excelling in equity (92.86) and pedagogical value (79.22), yielding the highest learning revision and transfer gains. Model_B shows exceptional privacy protection (95.48) and perfect policy transparency, making it well suited for privacysensitive contexts. Conversely, Model_C fails the deployment criterion because of inadequate privacy (66.78) and pedagogical value (69.16), along with the highest bias incident rate and the weakest learning outcomes. The findings revealed that technical accuracy alone is insufficient for educational deployment; a noncompensatory, multi-pillar evaluation is essential to ensure AI assessments are equitable, secure, and pedagogically sound. Read More Read Less

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