Speaker
Description
AI agents are beginning to move beyond writing isolated pieces of code toward executing substantial portions of high-energy physics analyses. Starting from the basic agentic loop - setting goals, using tools, executing code, inspecting results, and iterating - we examine increasingly sophisticated demonstrations, including experiment-software tasks and end-to-end autonomous analyses. This is a story of both progress and limitations: plausible results may contain hidden assumptions, validation gaps, non-determinism, or dependence on rapidly changing models. Scientifically useful agents are likely to require grounded collaboration knowledge, executable tests, independent review, reproducible workflows, and explicit human approval. This leads us to the next "question": how we build the infrastructure and controls needed to make their contributions trustworthy while preserving human responsibility and creativity for scientific progress.