Jul 2026· Practice and Experience in Advanced Research Computing· pp. 1-3· 0 citations· 12 references
Computer Science
TL;DR
It is argued that proactive engagement, not prohibition, is the path forward for facilitators who wish to remain relevant in the agentic era.
Abstract
Large language models have evolved from curiosity to co-pilot in under four years. With the emergence of agentic AI systems that reason, plan, and execute multi-step tasks autonomously, HPC centers face a new category of user need: researchers expect these tools to be supported, not just permitted. This paper offers a practitioner’s perspective from Purdue’s Rosen Center for Advanced Computing (RCAC), where we have begun deploying system-wide configurations, custom MCP servers, and user guidance for agentic tools. As a demonstration, every word of this manuscript was produced through an agent-first workflow: over one hundred commits of iterative collaboration between human authors and AI agents, documented in a public GitHub repository. We argue that proactive engagement, not prohibition, is the path forward for facilitators who wish to remain relevant in the agentic era.
This paper presents a hierarchical, dynamic architecture and software to discover resources across diverse cloud, edge, and HPC systems and exemplifies the importance of careful coordination between agents, discovery tools, and infrastructure for agentic science.
A unified, taxonomy-driven, and deployment-oriented survey of agentic AI systems, synthesizing recent advances through a modular reference architecture and a four-dimensional taxonomy that characterizes agents along the axes of autonomy, tool use, collaboration, and safety–governance is presented.
Sparsh Bajoria, Shreyanshu Ranjan, Adhitya M et al.· Cognitive Computation· 0 citations
This panel will convene leading researchers and practitioners to discuss the future of data agents, which is defined as an autonomous system capable of perceiving data in various forms, planning and executing complex data manipulation and analysis tasks, and interacting with humans through natural language or other int...
Guo-Liang Li, Yu-Yu Luo· Proceedings of the VLDB Endo...· 0 citations
The implications of this shift in mindset are explored and lessons learned are shared that hopefully illustrate this shift in emphasis with current agentic technology.
Overall, it is found that the use of coding agents in scientific computing holds great promise for accelerating scientific research and increasing the reliability of critical systems, but that outstanding concerns remain.
Jeremiah H. Li, Alex Rubinsteyn, Sergey Feldman et al.· bioRxiv· 0 citations
This tutorial introduces the workflow and architecture of agentic memory systems, and summarizes their operators, storage, and optimization techniques, aiming to inspire further innovation and progress in this exciting field.
Guo-Liang Li, Jia-Qi Tian, Xuan-He Zhou· Proceedings of the VLDB Endo...· 0 citations
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