CLIO is the agent layer of IOWarp, GRC's platform for managing scientific data and context for AI. In February 2026 it had two parts, a storage runtime and a tool kit. By November 13 it had six, each with its own release schedule, and together they had published more than 300 tagged releases on GitHub this year. This is a tour of what each part does and how it got there.
SC26, the International Conference for High Performance Computing, Networking, Storage and Analysis, runs November 15 to 20 at McCormick Place in Chicago, less than two miles from the Illinois Tech campus. GRC brings six workshop talks, four research posters and one paper, plus a live SPOTTER-AI demo at the DOE booth and an HDF5 birds-of-a-feather session with a CLIO demo. This guide lists every session by day, with times in Chicago time (CST).
AI agents are starting to do scientific work: finding data, running tools and producing results. That raises systems questions. Does a workflow of agents recover when one of them makes a mistake? How does an agent find the right file? Can storage use the time an agent spends thinking? GRC students answered all three in papers at IEEE eScience 2026, along with a fourth on in situ analysis, and a fifth paper at PACT 2026 addressed transformers on GPUs.
GRC researchers and collaborators presented twelve posters at the 13th Greater Chicago Area Systems Research Workshop (GCASR 2026) on May 11 at Northwestern University in Evanston. Taken together, the posters give a snapshot of the lab's work in spring 2026: how AI agents read and find scientific data, how multi-agent workflows run and recover, how workflows and logs move data, and how inference uses memory.
On February 17, 2026, Anthony Kougkas spoke at the monthly meeting of Earth Science Information Partners (ESIP) on context engineering for environmental data systems. His argument, first made at an Illinois Tech seminar six days earlier, was that AI agents in science fail or succeed on the quality of the context they receive, not on the size of the model.
The capability of artificial intelligence to undertake complex tasks is growing at a pace that defies easy extrapolation. For those of us who have spent careers building the infrastructure that makes large-scale computation possible, this moment feels less like a disruption and more like a culmination, the point at which the systems we build must finally become as intelligent as the science they serve. The question facing every PhD student, every research engineer, and every principal investigator in High-Performance Computing is not whether AI will reshape the research process, but whether they will be the ones directing that transformation.
This is the argument I want to make: the scarcest resource in modern research is no longer deep knowledge of a specific task. It is the ability to orchestrate compute, capital, data, and expert judgment to produce new outcomes. The age of the specialist executor is giving way to the age of the agent orchestrator, and the researchers who internalize this shift earliest will define the next decade of scientific discovery.
The Gnosis Research Center and Manish Parashar's group at the University of Utah are working together to bring IOWarp to the National Data Platform (opens in a new tab) (NDP). The collaboration is not separately funded; it grows out of shared interest in agentic data management for open science.
