Recent advances in artificial intelligence (AI) agents are pushing AI beyond tools toward autonomous scientific discovery. We discuss two complementary agentic systems for cosmology: \texttt{CMBEvolve}, which targets tasks with explicit quantitative objectives through LLM-guided code evolution and tree search, and \texttt{CosmoEvolve}, which targets open-ended scientific workflows through a virtual multi-agent research laboratory. As preliminary demonstrations, we apply \texttt{CMBEvolve} to out-of-distribution detection in weak-lensing maps, where it iteratively improves the benchmark score through code evolution, and \texttt{CosmoEvolve} to autonomous ACT DR6 data analysis, where it identifies non-trivial pair- and scale-dependent behaviour and produces analysis-grade diagnostics. These examples show how cosmology can provide both controlled benchmark tasks and realistic open-ended research problems for the development of AI scientist systems.

翻译：近期人工智能（AI）智能体的进展正将AI从工具推向自主科学发现。我们讨论了两个互补的智能体系统：\texttt{CMBEvolve}，通过基于大语言模型的代码进化与树搜索，专注于具有明确量化目标的任务；以及\texttt{CosmoEvolve}，通过虚拟多智能体研究实验室，专注于开放式科学工作流程。作为初步演示，我们将\texttt{CMBEvolve}应用于弱引力透镜地图中的分布外检测，通过代码进化迭代提升基准分数；将\texttt{CosmoEvolve}应用于ACT DR6数据的自主分析，识别出非平凡的对与尺度依赖行为，并生成分析级诊断结果。这些例子展示了宇宙学如何为AI科学家系统的发展提供受控的基准任务与真实的开放式研究问题。