- By:
- Nilsson, Paul; Boudreau, Joseph; Chowdhury, Tasnuva; Feng, Shengyu; Hoisie, Adolfy; Khan, Raees; Kim, Jaehyung; kilic, Ozgur; Klasky, Scott A; Klimentov, Alexei; Korchuganova, Tatiana; Maeno, Tadashi ; Martinez-Outschoorn, Verena; Park, David; Podhorszki, Norbert ; Ren, Yihui; Suter, Frederic ; Vatsavai, Sairam; Wenaus, T; Xue, Rui; Yang, Wei; Yang, Yiming; Yoo, Shinjae
- Journal Name:
- Journal of Instrumentation
- Volume:
- 21
- Issue Number:
- 7
- Publication Date:
- August 24, 2026
- View DOI Listing:
- https://doi.org/10.1088/1748-0221/21/07/C07030
Abstract
The PanDA workload management system, developed for large-scale distributed computing in high-energy physics, is being enhanced through the integration of AI-assisted operational tools built on the Model Context Protocol (MCP). This paper describes two complementary efforts. The first is PanDA MCP, a FastAPI-based interface layer that exposes PanDA REST APIs as standardized, self-describing MCP tools, bridging the synchronous PanDA backend with asynchronous AI clients. The second is Bamboo MCP, a modular plugin-based toolkit for AI-assisted operations, whose ATLAS plugin implements AskPanDA — a natural-language interface to the PanDA workload management system. Bamboo adopts a tool-first, evidence-driven architecture in which deterministic routing and structured data retrieval precede any LLM invocation. A key new capability enables natural-language queries against a live PanDA job database via an LLM-generated SQL pipeline protected by an AST-based security guard. A supervisor-managed suite of background agents maintains the local data stores on which these tools depend. The toolkit is experiment-agnostic by design, with plugins for ePIC, the Vera Rubin Observatory, and CGSim planned. A GPU-based testbed at Brookhaven National Laboratory supports co-development across the ATLAS and EIC communities.
