AgenticRS Architecture: Designing Intelligent Recommender Systems

The AgenticRS architecture introduces AutoModel, a groundbreaking framework designed to enhance the lifecycle management of industrial recommender systems. Unlike traditional systems that rely on static recall and ranking processes, AutoModel employs a dynamic approach through a network of interactive agents that possess long-term memory and self-improvement capabilities. This architecture features three primary agents: AutoTrain, which focuses on model creation and training; AutoFeature, dedicated to data analysis and the evolution of features; and AutoPerf, which oversees performance metrics, deployment, and online testing. A centralized coordination layer facilitates communication among these agents, documenting decisions, configurations, and results. A practical example, the paper autotrain module, illustrates how AutoTrain streamlines the process of reproducing models based on research papers by automating the transition from method interpretation to code generation, extensive training, and comparative analysis, significantly minimizing manual intervention. The AutoModel framework not only supports the autonomous evolution of large-scale recommender systems but also has potential applications in other AI domains, including search engines and advertising.