Large language model-based AI agents are now able to autonomously execute substantial portions of a high energy physics (HEP) analysis pipeline with minimal expert-curated input. Given access to a HEP dataset, an execution framework, and a corpus of prior experimental literature, we find that Claude Code succeeds in automating all stages of a typical analysis: event selection, background estimation, uncertainty quantification, statistical inference, and paper drafting. We argue that the experimental HEP community is underestimating the current capabilities of these systems, and that most proposed agentic workflows are too narrowly scoped or scaffolded to specific analysis structures. We present a proof-of-concept framework, Just Furnish Context (JFC), that integrates autonomous analysis agents with literature-based knowledge retrieval and multi-agent review, and show that this is sufficient to plan, execute, and document a credible high energy physics analysis. We demonstrate this by conducting analyses on open data from ALEPH, DELPHI, and CMS to perform electroweak, QCD, and Higgs boson measurements. Rather than replacing physicists, these tools promise to offload the repetitive technical burden of analysis code development, freeing researchers to focus on physics insight, truly novel method development, and rigorous validation. Given these developments, we advocate for new strategies for how the community trains students, organizes analysis efforts, and allocates human expertise.

翻译：基于大型语言模型的AI智能体现已能够在极少专家定制输入的情况下，自主完成高能物理分析流程的实质性环节。研究发现，当为Claude Code提供高能物理数据集、执行框架以及既往实验文献库时，它能成功自动化典型分析的全部阶段：事例选择、本底估计、不确定性量化、统计推断及论文撰写。我们认为，实验高能物理界当前低估了这些系统的实际能力，且多数拟议的智能体工作流在分析框架适配方面过于狭隘或结构化。我们提出概念验证框架"仅需提供背景"（JFC），该框架将自主分析智能体与基于文献的知识检索及多智能体审查机制相融合，证明其足以规划、执行并记录可信的高能物理分析。通过利用ALEPH、DELPHI及CMS实验的公开数据开展电弱、QCD及希格斯玻色子测量，我们验证了该方案的有效性。这些工具并非取代物理学家，而是旨在卸载分析代码开发中重复性的技术负担，使研究人员能专注于物理洞察、真正的方法创新及严谨验证。基于此发展态势，我们倡导学界在研究生培养、分析工作组织及人力资源配置方面制定新策略。