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Journal articles on the topic 'AI Agent Stack'

Author: Grafiati

Published: 7 June 2025

Last updated: 16 July 2025

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1

Prem Sai Reddy Kareti. "Trust Architecture for Enterprise AI Assistants: Technical Mechanisms for Transparency and Security." World Journal of Advanced Engineering Technology and Sciences 15, no. 3 (2025): 1880–87. https://doi.org/10.30574/wjaets.2025.15.3.0922.

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Enterprise AI assistants have become integral components of workplace software ecosystems, yet their successful adoption hinges on establishing genuine user trust. This article presents a comprehensive technical framework for implementing trust-building mechanisms within enterprise AI systems. The foundation of this framework consists of four interconnected pillars: explicit AI identity signaling, verifiable information provenance through citation systems, sensitivity-aware data handling capabilities, and secure context preservation during multi-agent handoffs. These mechanisms require thought

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2

Joshi, Satyadhar. "Using Gen AI Agents With GAE and VAE to Enhance Resilience of US Markets." International Journal of Computational Science, Information Technology and Control Engineering 12, no. 1 (2025): 23–38. https://doi.org/10.5121/ijcsitce.2025.12102.

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In this study, we explore the application of Generative AI (Gen AI) in enhancing interest rate models utilized in financial risk modeling. We employ advanced Gen AI Large Language Models (LLMs), including OpenAI's ChatGPT-4 and ChatGPT-4 Mini, as well as Google's Gemini versions 2.0 and 1.5, to generate pertinent queries and assess their accuracy. We propose and evaluate a prototype that leverages queries generated by publicly available LLMs to model and fine-tune parameters for Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), methodologies that can also be applied t

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Vedanbhatla, Naga V. K. Abhinav. "The Autonomous Stack: How Architects Are Enabling Self-Healing, Self-Optimizing Applications." European Journal of Computer Science and Information Technology 13, no. 47 (2025): 11–20. https://doi.org/10.37745/ejcsit.2013/vol13n471120.

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This article explores the emerging architectural paradigm of the "Autonomous Stack," where software systems are designed to be self-healing, self-optimizing, and resilient by default. As complexity increases across distributed cloud, edge, and AI-enabled environments, architects are leveraging observability, AI/ML, policy-driven orchestration, and event-driven patterns to enable systems that adapt and recover without manual intervention. The article covers key components such as service mesh, health probes, automated rollback mechanisms, and intelligent scaling. It also examines how predictive

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Kamath, M. S. Sandeep. "Real-time Performance Monitoring of HPC Clusters: Techniques and Challenges." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 06 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35707.

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In the era of exascale computing, High-Performance Computing (HPC) clusters have become essential for addressing complex computational challenges across various domains. The increasing scale and complexity of HPC systems pose significant challenges in ensuring optimal performance and resource utilization. Real-time performance monitoring has emerged as a critical requirement to address these challenges effectively. The paper offers a comprehensive analysis of real-time performance monitoring techniques and challenges within HPC clusters focusing on three key monitoring approaches: Prometheus f

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SERGIUS D, ROFINA. "AI-Powered Project Management and Reporting System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42174.

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Project management is a critical aspect of any organization, requiring efficient tracking of tasks, milestones, and team collaboration. Traditional project management systems often lack automation in reporting and data analysis. This paper presents an AI-powered Project Management and Reporting System that integrates MySQL for structured data storage, utilizes the LLM for AI-based report generation, and employs Python-docx for professional document creation. The system automates the generation of structured reports, enhances project tracking, and facilitates efficient communication among stake

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Meyer, Artem. "METHODS, FRAMEWORKS AND VIRTUAL PLATFORMS IN DOMAIN OF PORTABLE SELF-DRIVING VEHICLES CONTROL ADJUSTED FOR UNDETERMINED DYNAMIC ENVIRONMENT CONDITIONS: LITERATURE REVIEW." Applied Mathematics and Control Sciences, no. 2 (June 28, 2019): 62–83. http://dx.doi.org/10.15593/2499-9873/2019.2.04.

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The goal of implementing autopilot system into vehicle is the automation of its movement on a dynamic landscape. Vehicle movement task can be considered as a particular case of a classification task, because the problem of every autopilot system is an optimal set of agent actions selection depending on the vehicle state. Over the recent years a great success has been achieved in self-driving vehicles learning. This article is about current approaches to implementation of autopilot system with the focus on portable vehicles. Particularly, it covers neural networks’ algorithms, appropriate for s

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Huang, Qi, Johnathan Le, Sarang Joshi, Jason Mendes, Ganesh Adluru, and Edward DiBella. "Arterial Input Function (AIF) Correction Using AIF Plus Tissue Inputs with a Bi-LSTM Network." Tomography 10, no. 5 (2024): 660–73. http://dx.doi.org/10.3390/tomography10050051.

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Background: The arterial input function (AIF) is vital for myocardial blood flow quantification in cardiac MRI to indicate the input time–concentration curve of a contrast agent. Inaccurate AIFs can significantly affect perfusion quantification. Purpose: When only saturated and biased AIFs are measured, this work investigates multiple ways of leveraging tissue curve information, including using AIF + tissue curves as inputs and optimizing the loss function for deep neural network training. Methods: Simulated data were generated using a 12-parameter AIF mathematical model for the AIF. Tissue cu

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Sajid, Muhammad Imran. "Stacy: A Voice AI Agent Conducting Risk Assessment for Small Business Insurance." Open Journal of Applied Sciences 15, no. 04 (2025): 834–53. https://doi.org/10.4236/ojapps.2025.154056.

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Sağbaş, Murat, and Sefer Aydogan. "Unveiling the Nuances: How Fuzzy Set Analysis Illuminates Passenger Preferences for AI and Human Agents in Airline Customer Service." Tourism and Hospitality 6, no. 1 (2025): 43. https://doi.org/10.3390/tourhosp6010043.

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This research tackles an essential gap in understanding how passengers prefer to interact with artificial intelligence (AI) or human agents in airline customer service contexts. Using a mixed-methods approach that combines statistical analysis with fuzzy set theory, we examine these preferences across a range of service scenarios. With data from 163 participants’ Likert scale responses, our qualitative analysis via fuzzy set methods complements the quantitative results from regression analyses, highlighting a preference model contingent on context: passengers prefer AI for straightforward, rou

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Benyamin, Yarin, Argaman Mordoch, Shahaf Shperberg, Wiktor Piotrowski, and Roni Stern. "Crafting a Pogo Stick in Minecraft with Heuristic Search (Extended Abstract)." Proceedings of the International Symposium on Combinatorial Search 17 (June 1, 2024): 261–62. http://dx.doi.org/10.1609/socs.v17i1.31571.

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Minecraft is a widely popular video game renowned for its intricate environment. The game's open-ended design allows the creation of unique tasks and challenges for the agents, providing a broad spectrum for researchers to experiment with different AI techniques and applications. Indeed, various Minecraft tasks have been posed as an AI challenge. Most AI research on Minecraft focused on either applying Reinforcement Learning (RL) to solve the problem, learning an action model for planning, or modeling the problem for a domain-independent planner. In this work, we focus on the combinatorial sea

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Viswanatha raju Sangaraju. "A Framework for Secure Data Processing Using AI Agents in Business Intelligence Applications." Economic Sciences 21, no. 1 (2025): 914–24. https://doi.org/10.69889/srn8qw78.

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Increased usage of Business Intelligence (BI) systems to make data-driven decisions has made data privacy and security a bigger concern. Due to the complexity and scale of BI environments today, traditional security mechanisms are not adequate. In this paper we introduce a new framework for using AI agents in BI applications for secure processing of data. Using a risk-based approach, the framework combines elements of machine learning and artificial intelligence to detect anomalous behavior, protect sensitive data, and automate compliance with privacy regulations — all in an integrated manner

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12

Lekka-Kowalik, Agnieszka. "Morality in the AI World." Law and Business 1, no. 1 (2021): 44–49. http://dx.doi.org/10.2478/law-2021-0006.

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Abstract AIs’ presence in and influence on human life is growing. AIs are seen more and more as autonomously acting agents, which creates a challenge to build ethics into their design. This paper defends the thesis that we need to equip AI with artificial conscience to make them capable of wise judgements. An argument is built in three steps. First, the concept of decision is presented, and second, the Asilomar Principles for AI development are analysed. It is then shown that to meet those principles AI needs the capability of passing moral judgements on right and wrong, of following that judg

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13

Eger, Markus, and Chris Martens. "Keeping the Story Straight: A Comparison of Commitment Strategies for a Social Deduction Game." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 14, no. 1 (2018): 24–30. http://dx.doi.org/10.1609/aiide.v14i1.13015.

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Social deduction games present a unique challenge for AI agents, because communication plays a central role in most of them, and deception plays a key role in game play. To be successful in such games, players need to come up with convincing stories, but also discern the truth of statements of other players and adapt to the information learned from them. In this paper we present an approach for virtual agents that have to determine how long to stick to their story in the light of information obtained from other players. We apply this approach to a particular social deduction game, One Night Ul

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Tiwari, Adarsh. ""Harnessing Artificial Intelligence for Stock Market Forecasting: A Machine Learning Approach to Predicting Financial Trends"." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44969.

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Predicting the stock market has never been easy with its turbulent waves and complications. Traditional approaches lately did not even have the mark by providing any reasonably accurate prediction for the very huge amount of data and so many external influences around. We will analyze how AI/ML can help predict the stock markets better in this article. We use different algorithms that form the class of ML, namely linear regression, decision trees, random forests and neural networks, on historical market data with the goals of drawing trends and extrapolating prediction. When we demonstrate the

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15

Harshad Vijay Pandhare. "Developing scalable quality assurance pipelines for AI systems: Leveraging LLMs in enterprise applications." World Journal of Advanced Research and Reviews 26, no. 1 (2025): 1871–94. https://doi.org/10.30574/wjarr.2025.26.1.1268.

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With Enterprises rapidly including Large Language Models (LLMs) in their core operations, from customer service to finance to healthcare to e-commerce, there is an urgent need to pay utmost attention to the scalability and robustness of quality assurance (QA) pipelines. LLMs are probabilistic, sensitive to the context, and non-deterministic, so traditional QA methods fail them. In this article, we look at what organizations can do to build scalable QA frameworks to address the peculiar requirements and possibilities of AI systems built on LLMs. We first look at what sets LLM-specific QA apart

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Levin, Kyla H., Nicolas van Kempen, Emery D. Berger, and Stephen N. Freund. "ChatDBG: Augmenting Debugging with Large Language Models." Proceedings of the ACM on Software Engineering 2, FSE (2025): 1892–913. https://doi.org/10.1145/3729355.

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Debugging is a critical but challenging task for programmers. This paper proposes ChatDBG, an AI-powered debugging assistant. ChatDBG integrates large language models (LLMs) to significantly enhance the capabilities and user-friendliness of conventional debuggers. ChatDBG lets programmers engage in a collaborative dialogue with the debugger, allowing them to pose complex questions about program state, perform root cause analysis for crashes or assertion failures, and explore open-ended queries like "why is x null?". To handle these queries, ChatDBG grants the LLM autonomy to "take the wheel":

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17

Goh, Kean S., Roberta L. Gibson, William Cox, Fred Gaffney, and Martin van der Grinten. "Control of Slugs in No-Till Corn, 1987." Insecticide and Acaricide Tests 13, no. 1 (1988): 205a. http://dx.doi.org/10.1093/iat/13.1.205a.

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Abstract No-till corn plots were located in a Unadilla silt-loam field of pH 5.6 and 1% organic matter in Big Flats, N.Y. The previous crop was no-till corn in which the corn stalk and leaf residues provided a dense mulch. On 23 Apr, the no-till field was prepared by applying paraquat at 0.5 lb (AI)/acre to kill various weed seedlings. The field was seeded on 30 Apr using a tractor drawn Buffalo No-Till Planter to seed 4 rows/pass at 30-inch row spacing. Seed drop was set at 27,800 kernels/acre and planted at 1.0-1.5 inches deep. Simultaneously, chlorpyrifos (Lorsban® 15G) was applied at 2 lb

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18

Siramgari, Dayakar. "Building the Future: Unveiling the AI Agent Stack." International Journal on Recent and Innovation Trends in Computing and Communication 12, no. 2 (2024). https://doi.org/10.5281/zenodo.14533422.

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The envisioned AI agent stack represents a transformative approach to building and deploying artificial intelligence systems, integrating critical components such as vertical agents, hosting and serving infrastructure, observability, agent frameworks, memory, tool libraries, sandboxes, model serving, and storage. This comprehensive architecture aims to enhance the efficiency, scalability, and functionality of AI agents in diverse applications and industries. Vertical agents provide specialized expertise, whereas robust hosting and observability ensure reliable performance and proactive ma

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19

Mary, S. Angel Latha, Hamesh Ranjan R, Rahul Rajkumar, Rupika K, and Sukanth K. "Multi Agent Research Team (MART)." International Journal of Innovative Science and Research Technology, May 14, 2025, 3536–40. https://doi.org/10.38124/ijisrt/25apr1658.

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Academic and industrial research processes are still time-consuming, concerning manual data collection, summarization, and contextual integration. This article presents MART (Multi-Agent Research Team), An AI system that facilitates end-to-end research work by a collaborative multi-agent system.MART employs autonomous agents, large language models (LLMs), and multimodal data processing to improve query enrichment, real-time data collection, summarization, and context analysis. The system is user-friendly uploaded files (DOCX, PDFs) and pics, parsed and processed by GPT-4o and LLaMA 3.3 70B. An

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20

Song, Garrison Hongyu, and Ajeet Jain. "Can artificial intelligence beat the stock market?" Studies in Economics and Finance, August 9, 2022. http://dx.doi.org/10.1108/sef-03-2022-0133.

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Purpose Academia and financial practitioners have mixed opinions about whether artificial intelligence (AI) can beat the stock market. The purpose of this paper is to investigate theoretically what would happen if AI has further evolved into a superior ability to predict the future more accurately than average investors. Design/methodology/approach A theoretical model in an endowment economy with two types of representative investors (traditional investors and AI investors) is proposed, and based on the model, a long-run survival analysis for both types of investors is implemented. Findings Th

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21

Alex Avelar, Ewerton, and Ricardo Vinícius Dias Jordão. "The role of artificial intelligence in the decision-making process: a study on the financial analysis and movement forecasting of the world’s largest stock exchanges." Management Decision, July 5, 2024. http://dx.doi.org/10.1108/md-09-2023-1625.

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PurposeThis paper aims to analyze the role and performance of different artificial intelligence (AI) algorithms in forecasting future movements in the main indices of the world’s largest stock exchanges.Design/methodology/approachDrawing on finance-based theory, an empirical and experimental study was carried out using four AI-based models. The investigation comprised training, testing and analysis of model performance using accuracy metrics and F1-Score on data from 34 indices, using 9 technical indicators, descriptive statistics, Shapiro–Wilk, Student’s t and Mann–Whitney and Spearman correl

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22

Shalaby, Ahmed. "The end of labor economics as we know it: DSGM and the shift to a 3D economy- a case study of Denmark’s transformation into a talent stock market." Digital Economy and Sustainable Development 3, no. 1 (2025). https://doi.org/10.1007/s44265-025-00066-5.

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Abstract The traditional labor market, constrained by time and place, is increasingly inadequate for the demands of the digital age. This study introduces the Digital Sustainable Growth Model (DSGM) as a transformative framework to transition from a two-dimensional (2D) economy to a three-dimensional (3D) economy, where virtual space enables dynamic value creation. By redefining individuals as talent—multi-dimensional agents capable of lifelong contributions—DSGM fosters a talent stock market driven by data, ideas, and interactions. Using Denmark, a pioneer in labor market flexicurity, as a ca

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