Allocating Agency: Structure-Preserving Multi-Agent Reasoning for Recommendation

Large language models (LLMs) have introduced a new paradigm of agentic recommendation, yet effectively integrating LLM agents into mature recommender systems remains challenging. Existing recommendation models already encode substantial intelligence through collaborative signals, retrieval mechanisms, and behavioral priors, making unrestricted LLM autonomy an insufficient solution. We formulate this challenge as the Agency Allocation Problem: how to assign LLM reasoning authority to unresolved recommendation decisions while preserving the intelligence already captured by specialized recommenders. Based on this perspective, we propose Agent-R, a structure-preserving framework that allocates LLM agency across two complementary decision spaces. In the Consideration Space, a Planning Agent coordinates heterogeneous recommendation evidence to adapt candidate coverage around a reliable retrieval backbone. In the Preference Space, a Ranking Agent learns contextual preference residuals over an explicit recommendation prior to refine ranking decisions without replacing existing structures. Experiments across diverse recommendation domains demonstrate that Agent-R consistently improves both candidate coverage and ranking quality, achieving up to 28.3% relative gains in Recall@10 over strong fusion baselines. Further analyses show that structured agency allocation enables LLMs to provide complementary reasoning while retaining the strengths of specialized recommendation models.