Speakers
Description
Agentic AI systems are rapidly moving beyond information retrieval and code completion toward the execution of complex, multi-step technical tasks. For experimental particle physics, this creates both an opportunity and a challenge: AI agents can accelerate analysis implementation, validation, and documentation, but physics measurements require controlled reasoning, reproducibility, and expert judgment rather than merely producing results that appear plausible. In this talk, we discuss recent work by the Electron-Positron Alliance using archived LEP data as a controlled testing ground for agentic AI in collider analysis. As a proof of concept, AI agents were used under expert physicist direction to perform a measurement of the thrust distribution in archived ALEPH e+e- collisions at sqrt(s) = 91.2 GeV, including event selection, correction procedures, uncertainty studies, unfolding, and analysis-note writing. We also discuss exploratory applications to DELPHI data, including particle-identification and K/pi studies, where fewer pre-existing analysis blueprints are available. The central focus is NOT whether AI can generate analysis code quickly, but how it can be integrated into a physics-rigorous workflow. We emphasize a conservative operating model in which the AI agent accelerates technical implementation while the physicist retains ownership of analysis choices, validates each step, and controls the scope through small, inspectable tasks. These studies suggest that preserved e+e- datasets provide an ideal environment for developing practical standards for AI-assisted physics analysis: transparent workflows, human-in-the-loop validation, robust guardrails, and reproducible outputs. More broadly, they point toward a future theory-experiment loop in which AI agents assist with calculations, measurements, comparisons, and documentation, while scientific judgment remains firmly in human hands.