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：大型语言模型智能体正在重塑工业格局。然而，由于任务差异巨大，大多数实际应用的智能体仍需人工设计，导致构建过程劳动密集。这一现状引出一个核心问题：我们能否在真实环境中自动创建和适配领域智能体？尽管近期多种方法致力于自动化智能体创建，但它们通常将智能体生成视为黑盒过程，仅依赖最终性能指标指导优化。这类策略忽略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。针对这些局限，我们提出ReCreate——一种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，这些历史数据提供了关于成败原因及改进途径的丰富具体信号。具体而言，我们引入了一种"智能体即优化器"范式，通过三个核心组件有效从经验中学习：(i) 按需检索的经验存储与检索机制；(ii) 将执行经验映射为框架编辑的推理-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。在跨域实验中，即使从最小种子框架出发，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。