NSF AI grant $2.54M: "Cloud Conversations" — an AI assistant that builds research clouds by chatting
The NSF awarded about $2.54M to "Cloud Conversations," an AI conversational assistant that lets researchers describe what they need in everyday language and then builds and verifies the environment on shared research cloud infrastructure. It lowers the specialized burden (system administration, networking, security) of cloud setup and improves research productivity and reproducibility.
Grant overview (primary data)
- Award amount$2,540,265
- RecipientUniversity of Chicago(IL)
- ProgramSoftware Institutes
- Period2026-10-01 〜 2029-09-30
- FunderU.S. National Science Foundation (NSF) / NSF
Key points
- An AI conversational assistant that builds and verifies research-cloud environments from plain-language requirements
- Lowers the system-admin/networking/security burden of cloud setup; improves productivity and reproducibility
- An AI agent framework that plans, provisions, and validates on Chameleon and Jetstream2
- Planning modules with resource/timing/hardware checks, state/error handling, and search pipelines
- About $2.54M; Software Institutes; Illinois; from 2026
The NSF awarded about $2,540,265 to "Cloud Conversations: AI-Augmented Interfaces to Research Infrastructure" (NSF Award 2609115; program: Software Institutes [Frameworks / collaborative research]; Illinois; starting October 2026).
Per the abstract, cloud computing is essential to a growing number of science use cases, but configuring scientific environments for the cloud is challenging — requiring specialized knowledge in system administration, networking, and security and involving numerous configuration settings. Many scientific workloads also depend on tightly coupled virtual clusters, specialized hardware, fast interconnects, accelerators, and custom drivers. Managing this complexity consumes time and attention researchers could otherwise devote to science. The project develops an AI-based conversational assistant for configuring scientific computing environments: researchers describe what they need in everyday language, and it creates the environment and verifies its integrity on shared research cloud infrastructure — increasing productivity, lowering the cost of using cloud computing, and enabling practical reproducibility of computational experiments.
Technically, it designs and deploys an AI-based agent framework that can plan, provision, and validate scientific computing environments on open research computing infrastructure such as Chameleon and Jetstream2. The framework combines large language models running on open, high-performance academic hardware with software tools exposed through standard interfaces — cloud-based provisioning services, hardware/software environment templates, correctness checks, and a validation benchmark suite. Key components include planning modules with built-in checks on resource limits, timing, and hardware compatibility; state- and error-handling modules that track multi-step workflows and summarize system events; and search pipelines that organize information from documentation, logs, help-desk tickets, and environment artifacts into a searchable knowledge base.
Why it matters
A case applying LLM agents to building and operating research infrastructure. For those tracking AI agents, natural-language infrastructure automation (IaC), automated cloud configuration, and research reproducibility, a useful read on real-world implementation direction in U.S. research.
FAQ
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Sources (primary)
Source: NSF Award Search (U.S. National Science Foundation, public domain). Amounts are the obligated amount. For privacy, we do not handle principal investigator names.
- NSF Award (original, official)
- NSF Award ID: 2609115