Speaker
Description
Agentic artificial intelligence (AI) systems are emerging as a practical middle layer for intelligent, self-optimizing accelerator operations. Building on work at the Advanced Light Source, we have developed a modular agentic framework that integrates natural-language interfaces with control systems, archival data, simulation tools, and technical documentation, enabling context-aware reasoning with human-in-the-loop execution. This approach provides intuitive access to complex accelerator environments while preserving safety, transparency, and reproducibility.
A central focus of the framework is usability and rapid onboarding. Self-contained tutorials, reproducible deployment patterns, and a facility-agnostic interface allow laboratories to adopt agentic workflows with minimal customization. This streamlined process has supported deployments at APS, SLAC, SNS, CEBAF, ALS, and BELLA as part of a DOE/MOAT effort within the Genesis mission, where agents execute multi-step tasks, generate inspectable plans, and analyze historical and live data through a shared language interface.
This contribution presents the core architecture, cross-facility deployment experience, and early operational lessons from these implementations. It also outlines how agentic workflows form a unifying layer for emerging capabilities, such as physics-informed optimizers, reinforcement-learning agents, and automated tuning assistants, advancing autonomous control in next-generation scientific facilities.
| In which format do you inted to submit your paper? | LaTeX |
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