Governed By Agents: A Survey On The Role Of Agentic AI In Future Computing Environments

• URL: http://arxiv.org/abs/2509.16676v1
• Date: Sat, 20 Sep 2025 13:03:11 GMT
• Title: Governed By Agents: A Survey On The Role Of Agentic AI In Future Computing Environments
• Authors: Nauman Ali Murad, Safia Baloch,
• Abstract summary: Agentic AI has the potential to reduce reliance on extremely large (public) cloud environments.<n>Many of these likely migrations will be spurred by factors like on-premises processing needs, diminished data consumption footprints, and cost savings.<n>This study examines how a solution for implementing AI's autonomy could result in a re-architecture of the systems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The emergence of agentic Artificial Intelligence (AI), which can operate autonomously, demonstrate goal-directed behavior, and adaptively learn, indicates the onset of a massive change in today's computing infrastructure. This study investigates how agentic AI models' multiple characteristics may impact the architecture, governance, and operation under which computing environments function. Agentic AI has the potential to reduce reliance on extremely large (public) cloud environments due to resource efficiency, especially with processing and/or storage. The aforementioned characteristics provide us with an opportunity to canvas the likelihood of strategic migration in computing infrastructures away from massive public cloud services, towards more locally distributed architectures: edge computing and on-premises computing infrastructures. Many of these likely migrations will be spurred by factors like on-premises processing needs, diminished data consumption footprints, and cost savings. This study examines how a solution for implementing AI's autonomy could result in a re-architecture of the systems and model a departure from today's governance models to help us manage these increasingly autonomous agents, and an operational overhaul of processes over a very diverse computing systems landscape that bring together computing via cloud, edge, and on-premises computing solutions. To enable us to explore these intertwined decisions, it will be fundamentally important to understand how to best position agentic AI, and to navigate the future state of computing infrastructures.

Related papers

• Federated Agentic AI for Wireless Networks: Fundamentals, Approaches, and Applications [60.721304295812445]
  Federated learning (FL) has the potential to improve the overall loop of agentic AI.<n>We first summarize fundamentals of agentic AI and mainstream FL types. Then, we illustrate how each FL type can strengthen a specific component of agentic AI's loop.<n>We conduct a case study on using FRL to improve the performance of agentic AI's action decision in low-altitude wireless networks.
  arXiv  Detail & Related papers  (2026-03-02T11:26:56Z)
• Securing AI Agents: Implementing Role-Based Access Control for Industrial Applications [0.0]
  In industrial settings, AI agents are transforming operations by enhancing decision-making, predictive maintenance, and process optimization.<n>Despite these advancements, AI agents remain vulnerable to security threats, including prompt injection attacks.<n>This paper proposes a framework for integrating Role-Based Access Control (RBAC) into AI agents, providing a robust security guardrail.
  arXiv  Detail & Related papers  (2025-09-14T20:58:08Z)
• Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunities [117.49715661395294]
  Data structurization can play a promising role by transforming intricate and disorganized data into well-structured forms.<n>This survey presents a first systematic review of how graphs can empower AI agents.
  arXiv  Detail & Related papers  (2025-06-22T12:59:12Z)
• Towards Pervasive Distributed Agentic Generative AI -- A State of The Art [0.0]
  The rapid advancement of intelligent agents and Large Language Models (LLMs) is reshaping the pervasive computing field.<n>This survey outlines the architectural components of LLM agents and examines their deployment and evaluation across various scenarios.<n>It highlights state-of-the-art agent deployment strategies and applications, including local and distributed execution on resource-constrained devices.
  arXiv  Detail & Related papers  (2025-06-16T10:15:06Z)
• Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey [58.50944604905037]
  Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications.<n>Recent advancements in AI, particularly deep learning and large language models (LLMs), have dramatically enhanced the capabilities of these distributed systems.<n>This survey provides a structured tutorial on fundamental architectures, enabling technologies, and emerging applications.
  arXiv  Detail & Related papers  (2025-05-03T13:55:38Z)
• AIOpsLab: A Holistic Framework to Evaluate AI Agents for Enabling Autonomous Clouds [12.464941027105306]
  AI for IT Operations (AIOps) aims to automate complex operational tasks, such as fault localization and root cause analysis, to reduce human workload and minimize customer impact.<n>Recent advances in Large Language Models (LLMs) and AI agents are revolutionizing AIOps by enabling end-to-end and multitask automation.<n>We present AIOPSLAB, a framework that deploys microservice cloud environments, injects faults, generates workloads, and exports telemetry data but also orchestrates these components and provides interfaces for interacting with and evaluating agents.
  arXiv  Detail & Related papers  (2025-01-12T04:17:39Z)
• Building AI Agents for Autonomous Clouds: Challenges and Design Principles [17.03870042416836]
  AI for IT Operations (AIOps) aims to automate complex operational tasks, like fault localization and root cause analysis, thereby reducing human intervention and customer impact. This vision paper lays the groundwork for such a framework by first framing the requirements and then discussing design decisions. We propose AIOpsLab, a prototype implementation leveraging agent-cloud-interface that orchestrates an application, injects real-time faults using chaos engineering, and interfaces with an agent to localize and resolve the faults.
  arXiv  Detail & Related papers  (2024-07-16T20:40:43Z)
• Computing Power and the Governance of Artificial Intelligence [51.967584623262674]
  Governments and companies have started to leverage compute as a means to govern AI. compute-based policies and technologies have the potential to assist in these areas, but there is significant variation in their readiness for implementation. naive or poorly scoped approaches to compute governance carry significant risks in areas like privacy, economic impacts, and centralization of power.
  arXiv  Detail & Related papers  (2024-02-13T21:10:21Z)
• Scalable, Distributed AI Frameworks: Leveraging Cloud Computing for Enhanced Deep Learning Performance and Efficiency [0.0]
  In recent years, the integration of artificial intelligence (AI) and cloud computing has emerged as a promising avenue for addressing the growing computational demands of AI applications. This paper presents a comprehensive study of scalable, distributed AI frameworks leveraging cloud computing for enhanced deep learning performance and efficiency.
  arXiv  Detail & Related papers  (2023-04-26T15:38:00Z)
• AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges [60.56413461109281]
  Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes. We discuss in depth the key types of data emitted by IT Operations activities, the scale and challenges in analyzing them, and where they can be helpful. We categorize the key AIOps tasks as - incident detection, failure prediction, root cause analysis and automated actions.
  arXiv  Detail & Related papers  (2023-04-10T15:38:12Z)
• Edge-Cloud Polarization and Collaboration: A Comprehensive Survey [61.05059817550049]
  We conduct a systematic review for both cloud and edge AI. We are the first to set up the collaborative learning mechanism for cloud and edge modeling. We discuss potentials and practical experiences of some on-going advanced edge AI topics.
  arXiv  Detail & Related papers  (2021-11-11T05:58:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.