Large Language Model agents are reshaping the industrial landscape. However, most practical agents remain human-designed because tasks differ widely, making them labor-intensive to build. This situation poses a central question: can we automatically create and adapt domain agents in the wild? While several recent approaches have sought to automate agent creation, they typically treat agent generation as a black-box procedure and rely solely on final performance metrics to guide the process. Such strategies overlook critical evidence explaining why an agent succeeds or fails, and often require high computational costs. To address these limitations, we propose ReCreate, an experience-driven framework for the automatic creation of domain agents. ReCreate systematically leverages agent interaction histories, which provide rich concrete signals on both the causes of success or failure and the avenues for improvement. Specifically, we introduce an agent-as-optimizer paradigm that effectively learns from experience via three key components: (i) an experience storage and retrieval mechanism for on-demand inspection; (ii) a reasoning-creating synergy pipeline that maps execution experience into scaffold edits; and (iii) hierarchical updates that abstract instance-level details into reusable domain patterns. In experiments across diverse domains, ReCreate consistently outperforms human-designed agents and existing automated agent generation methods, even when starting from minimal seed scaffolds.

翻译：大语言模型智能体正在重塑产业格局。然而，由于任务差异巨大，构建过程劳动密集，目前大多数实用智能体仍由人工设计。这一现状引出了一个核心问题：我们能否在开放环境中自动创建并适配领域智能体？尽管近期已有若干方法尝试自动化智能体创建，但它们通常将智能体生成视为黑箱过程，仅依赖最终性能指标指导生成。此类策略忽视了解释智能体成败的关键证据，且往往需要高昂的计算成本。为应对这些局限，我们提出ReCreate——一个由经验驱动的领域智能体自动创建框架。ReCreate系统性地利用智能体交互历史，这些历史既提供了成功或失败原因的具体信号，也揭示了改进路径。具体而言，我们引入了一种智能体即优化器的范式，通过三个关键组件实现从经验中高效学习：（i）支持按需检查的经验存储与检索机制；（ii）将执行经验映射为脚手架编辑的推理-创建协同流水线；（iii）将实例级细节抽象为可复用领域模式的分层更新机制。在跨多个领域的实验中，即使从最小初始脚手架开始，ReCreate也始终优于人工设计的智能体及现有自动化智能体生成方法。