Algorithm Optimization of AI Agent in Customer Behavior Prediction Based on Large Model

With the rapid development of AI technology, especially the application of Large Language Model (LLM), AI Agent shows great potential in the field of customer behavior prediction. Firstly, this paper reviews the traditional methods of customer behavior prediction and points out its limitations. Subsequently, this paper proposes an algorithm optimization strategy that combines multimodal data fusion, adaptive attention mechanism and self-optimization framework based on reinforcement learning (RL). The experimental results show that the prediction accuracy of this strategy on Online Retail data set is improved from 82.5% to 87.3%, and the F1 score is improved from 0.83 to 0.88. On the MovieLens data set, the mean square error (MSE) is reduced from 0.95 to 0.78, and the standard deviation is reduced by about 30%, which improves the stability of the algorithm. Although remarkable achievements have been made, algorithm optimization still has limitations in dealing with extremely unbalanced data sets and computing resource dependence. Future research will explore more efficient model compression technology and special processing methods for unbalanced data sets to further improve the performance and applicability of the algorithm.