Lifetime Value (LTV) prediction is critical in advertising, recommender systems, and e-commerce. In practice, LTV data patterns vary across decision scenarios. As a result, practitioners often build complex, scenario-specific pipelines and iterate over feature processing, objective design, and tuning. This process is expensive and hard to transfer. We propose AgentLTV, an agent-based unified search-and-evolution framework for automated LTV modeling. AgentLTV treats each candidate solution as an {executable pipeline program}. LLM-driven agents generate code, run and repair pipelines, and analyze execution feedback. Two decision agents coordinate a two-stage search. The Monte Carlo Tree Search (MCTS) stage explores a broad space of modeling choices under a fixed budget, guided by the Polynomial Upper Confidence bounds for Trees criterion and a Pareto-aware multi-metric value function. The Evolutionary Algorithm (EA) stage refines the best MCTS program via island-based evolution with crossover, mutation, and migration. Experiments on a large-scale proprietary dataset and a public benchmark show that AgentLTV consistently discovers strong models across ranking and error metrics. Online bucket-level analysis further indicates improved ranking consistency and value calibration, especially for high-value and negative-LTV segments. We summarize practitioner-oriented takeaways: use MCTS for rapid adaptation to new data patterns, use EA for stable refinement, and validate deployment readiness with bucket-level ranking and calibration diagnostics. The proposed AgentLTV has been successfully deployed online.

翻译：生命周期价值（LTV）预测在广告、推荐系统和电子商务中至关重要。实践中，LTV数据模式随决策场景变化而变化。因此，从业者通常需要构建复杂且针对特定场景的流水线，并在特征处理、目标设计和调优环节反复迭代。这一过程成本高昂且难以迁移。我们提出AgentLTV，一种基于智能体的统一搜索与进化框架，用于自动化LTV建模。AgentLTV将每个候选解决方案视为一个{可执行的流水线程序}。由大语言模型驱动的智能体负责生成代码、运行与修复流水线，并分析执行反馈。两个决策智能体协调一个两阶段搜索过程：蒙特卡洛树搜索阶段在固定预算下，依据多项式上置信树准则和帕累托感知的多指标价值函数引导，探索广阔的建模选择空间；进化算法阶段则通过基于岛屿的进化策略（包含交叉、变异和迁移）对最佳MCTS程序进行精细化改进。在大规模专有数据集和公开基准测试上的实验表明，AgentLTV在排序和误差指标上均能持续发现强效模型。在线桶级分析进一步表明，其在排序一致性和价值校准方面有所提升，尤其针对高价值和负LTV用户群体。我们总结了面向从业者的关键启示：使用MCTS快速适应新数据模式，利用EA进行稳定优化，并通过桶级排序与校准诊断验证部署就绪度。所提出的AgentLTV已成功在线部署。