Relevant bibliographies by topics / AI Agent Stack

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Academic literature on the topic 'AI Agent Stack'

Author: Grafiati
Published: 7 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "AI Agent Stack"

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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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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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Books on the topic "AI Agent Stack"

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Brachman, Ronald J., and Hector J. Levesque. Machines like Us. The MIT Press, 2022. http://dx.doi.org/10.7551/mitpress/14299.001.0001.

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How we can create artificial intelligence with broad, robust common sense rather than narrow, specialized expertise. It's sometime in the not-so-distant future, and you send your fully autonomous self-driving car to the store to pick up your grocery order. The car is endowed with as much capability as an artificial intelligence agent can have, programmed to drive better than you do. But when the car encounters a traffic light stuck on red, it just sits there—indefinitely. Its obstacle-avoidance, lane-following, and route-calculation capacities are all irrelevant; it fails to act because it lac
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Book chapters on the topic "AI Agent Stack"

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Clodic, Aurelie, and Rachid Alami. "What Is It to Implement a Human-Robot Joint Action?" In Robotics, AI, and Humanity. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-54173-6_19.

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AbstractJoint action in the sphere of human–human interrelations may be a model for human–robot interactions. Human–human interrelations are only possible when several prerequisites are met, inter alia: (1) that each agent has a representation within itself of its distinction from the other so that their respective tasks can be coordinated; (2) each agent attends to the same object, is aware of that fact, and the two sets of “attentions” are causally connected; and (3) each agent understands the other’s action as intentional. The authors explain how human–robot interaction can benefit from the same threefold pattern. In this context, two key problems emerge. First, how can a robot be programed to recognize its distinction from a human subject in the same space, to detect when a human agent is attending to something, to produce signals which exhibit their internal state and make decisions about the goal-directedness of the other’s actions such that the appropriate predictions can be made? Second, what must humans learn about robots so they are able to interact reliably with them in view of a shared goal? This dual process is here examined by reference to the laboratory case of a human and a robot who team up in building a stack with four blocks.
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Ingle, Vaishali. "Multi-Agent Trading System Using Artificial Intelligence." In AI in the Social and Business World: A Comprehensive Approach. BENTHAM SCIENCE PUBLISHERS, 2024. http://dx.doi.org/10.2174/9789815256864124010010.

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Multi-agent systems are concerned with decision-making tasks where multiple agents act in a shared environment. Agents can observe their environment (partially or fully), act to impact the environment, and might have different or aligned goals. Multi-Agent Systems Artificial Intelligence (MAAI) is used for simulating enduser requirements. The models designed are examples of the use of AI in the business world. The concept of reinforcement learning can be applied to stock price prediction for a specific stock, working in an agent-based system to predict higher returns based on the current environment. The agent's reward will be either profit or loss. A multi-agent system will use three types of agents: agent 1 (forecasting agent using a basic machine learning algorithm), agent 2 (judgmental agent; the background algorithms to work on it are reinforcement learning or fuzzy neural networks), and agent 3 (based on simple trading rules or neural networks). Alert Agent (AA) guarantees proficient conveying inside the schema. Signals are one of the alerts. The alert agent sends the foundation agents (Agent 1, Agent 2, and Agent 3) signals (verdict) delivered by the superior agent. Depending on these verdicts, the superior policies are understood to be presented to the users (traders). Depending on the verdict by Superior, investment risk can be minimized. The multi-agent framework verdict is combined with sentiment collected from finance news for a particular company. The cognizant behavior of agents in the stock market is also considered future research work for this framework. AI-based stock trading systems must be strengthened in the future with the use of various security measures.
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Conference papers on the topic "AI Agent Stack"

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Ramachandran, Nivetha, and M. Thirumaran. "AI-Enhanced Pod Scheduling: Optimizing MERN and MEAN Stack Performance in Kubernetes." In 2025 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI). IEEE, 2025. https://doi.org/10.1109/icdsaai65575.2025.11011851.

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Wawer, Michał, and Jarosław Chudziak. "Integrating Traditional Technical Analysis with AI: A Multi-Agent LLM-Based Approach to Stock Market Forecasting." In 17th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013191200003890.

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de Oliveira, Renato Arantes, Heitor S. Ramos, Daniel Hasan Dalip, and Adriano César Machado Pereira. "A tabular sarsa-based stock market agent." In ICAIF '20: ACM International Conference on AI in Finance. ACM, 2020. http://dx.doi.org/10.1145/3383455.3422559.

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Fatemi, Sorouralsadat, and Yuheng Hu. "FinVision: A Multi-Agent Framework for Stock Market Prediction." In ICAIF '24: 5th ACM International Conference on AI in Finance. ACM, 2024. http://dx.doi.org/10.1145/3677052.3698688.

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Vicente, Óscar Fernández, Fernando Fernández Rebollo, and Francisco Javier García Polo. "Deep Q-learning market makers in a multi-agent simulated stock market." In ICAIF'21: 2nd ACM International Conference on AI in Finance. ACM, 2021. http://dx.doi.org/10.1145/3490354.3494448.

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Yagi, Isao, Mahiro Hoshino, and Takanobu Mizuta. "Analysis of the impact of maker-taker fees on the stock market using agent-based simulation." In ICAIF '20: ACM International Conference on AI in Finance. ACM, 2020. http://dx.doi.org/10.1145/3383455.3422523.

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Fernandez Berrocal, Miguel, Alina Shashel, Muhammad Usama, et al. "Autonomous Directional Drilling Simulator Development for the Drillbotics 2021-2022 Virtual Competition." In SPE/IADC International Drilling Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/212507-ms.

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Abstract The work focuses on the drilling control algorithms as well as Artificial Intelligence (AI) technique implementation into an in-house real-time drilling simulator developed by the Drillbotics® Virtual Rig Team from the University of Stavanger, the winner of 2021-2022 Drillbotics Competition. The designed simulator consists of a topside model capable of calculating block position, surface hookload, surface torque, and bottom hole pressure. To achieve drilling efficiency, a formation-based rate of penetration (ROP) optimization module is running in real-time, where the safe-operational
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