AgenticRS-Architecture: System Design for Agentic Recommender Systems
On the map: LLM agents and tool use → Agentic recommender systems
Abstract (arXiv)
AutoModel is an agent based architecture for the full lifecycle of industrial recommender systems. Instead of a fixed recall and ranking pipeline, AutoModel organizes recommendation as a set of interacting evolution agents with long term memory and self improvement capability. We instantiate three core agents along the axes of models, features, and resources: AutoTrain for model design and training, AutoFeature for data analysis and feature evolution, and AutoPerf for performance, deployment, and online experimentation. A shared coordination and knowledge layer connects these agents and records decisions, configurations, and outcomes. Through a case study of a module called paper autotrain, we show how AutoTrain automates paper driven model reproduction by closing the loop from method parsing to code generation, large scale training, and offline comparison, reducing manual effort for method transfer. AutoModel enables locally automated yet globally aligned evolution of large scale recommender systems and can be generalized to other AI systems such as search and advertising.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Agentic recommender systems, one of 16 directions in LLM agents and tool use. Builds on 19 of its 22 references that are in the map. First posted on arXiv 27 Mar 2026.
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19 of 22 references.
References · 19
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7 more
- 2025OneRec Technical Reportcited by 24
- 2025REG4Rec: Reasoning-Enhanced Generative Model for Large-Scale Recommendation Systemscited by 18
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- 2026Rethinking Recommendation Paradigms: From Pipelines to Agentic Recommender Systems
- 2025Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models
- 2026EvoRec: Self Evolving Agentic Recommender Systems
- 2026Autonomous Information Seeking: A Roadmap for Agentic Recommender Systems
- 2026Auto-RecSys: Harnessing Autonomous Research Agents for Industry-Scale Recommender System
- 2025AgentRecBench: Benchmarking LLM Agent-based Personalized Recommender Systems
- 2026Self-Evolving Recommendation System: End-To-End Autonomous Model Optimization With LLM Agents
- 2025The Future is Agentic: Definitions, Perspectives, and Open Challenges of Multi-Agent Recommender Systems