Agentic evolution has emerged as a powerful paradigm for improving programs, workflows, and scientific solutions by iteratively generating candidates, evaluating them, and using feedback to guide future search. However, existing methods are typically instantiated either as fixed hand-designed procedures that are modular but rigid, or as general-purpose agents that flexibly integrate feedback but can drift in long-horizon evolution. Both forms accumulate rich evidence over time, including candidates, feedback, traces, and failures, yet lack a stable interface for organizing this evidence and revising the mechanism that drives future evolution. We address this limitation by formulating agentic evolution as an interactive environment, where the accumulated evolution context serves as a process-level state. We introduce AEvo, a harnessed meta-editing framework in which a meta-agent observes this state and acts not by directly proposing the next candidate, but by editing the procedure or agent context that controls future evolution. This unified interface enables AEvo to steer both procedure-based and agent-based evolution, making accumulated evidence actionable for long-horizon search. Empirical evaluations on agentic and reasoning benchmarks show that AEvo outperforms five evolution baselines, achieving a 26 relative improvement over the strongest baseline. Across three open-ended optimization tasks, AEvo further outperforms four evolution baselines and achieves state-of-the-art performance under the same iteration budget.

翻译：智能体进化已成为一种强大的范式，通过迭代生成候选方案、评估它们并利用反馈来引导未来搜索，从而改进程序、工作流程和科学解决方案。然而，现有方法通常要么实例化为固定的手工设计流程（模块化但僵化），要么实例化为通用智能体（灵活整合反馈但可能在长周期进化中偏离方向）。这两种形式都会随时间积累丰富证据，包括候选方案、反馈、轨迹和失败记录，但缺乏稳定的接口来组织这些证据并修正驱动未来进化的机制。我们通过将智能体进化构建为一个交互式环境来解决这一局限，其中积累的进化上下文充当过程级状态。我们提出AEvo，一个受控的元编辑框架，其中元智能体观察此状态并采取行动——不是直接提出下一个候选方案，而是编辑控制未来进化的流程或智能体上下文。这一统一接口使AEvo能够引导基于流程和基于智能体的进化，使累积证据在长周期搜索中具有可操作性。在智能体和推理基准上的实证评估显示，AEvo优于五种进化基线，相较于最强基线实现了26%的相对改进。在三个开放式优化任务中，AEvo进一步超越了四种进化基线，并在相同迭代预算下达到了最先进的性能。