The Future is Agentic: Definitions, Perspectives, and Open Challenges of Multi-Agent Recommender Systems

TL;DR

This paper explores how large language models evolving into agentic entities can revolutionize recommender systems through formal frameworks, use cases, and identifying key challenges for future research.

Contribution

It introduces a unified formalism for multi-agent recommender systems, presents practical use cases, and outlines open challenges and research directions for integrating agentic LLMs into recommendation pipelines.

Findings

Formal framework for agentic recommender systems

Four illustrative use cases demonstrating capabilities

Identification of key challenges like scalability and alignment

Abstract

Large language models (LLMs) are rapidly evolving from passive engines of text generation into agentic entities that can plan, remember, invoke external tools, and co-operate with one another. This perspective paper investigates how such LLM agents (and societies thereof) can transform the design space of recommender systems. We introduce a unified formalism that (i) models an individual agent as a tuple comprising its language core, tool set, and hierarchical memory, and (ii) captures a multi-agent recommender as a triple of agents, shared environment, and communication protocol. Within this framework, we present four end-to-end use cases-interactive party planning, synthetic user-simulation for offline evaluation, multi-modal furniture recommendation, and brand-aligned explanation generation-each illustrating a distinct capability unlocked by agentic orchestration. We then surface five cross-cutting challenge families: protocol complexity, scalability, hallucination and error propagation, emergent misalignment (including covert collusion), and brand compliance. For each, we formalize the problem, review nascent mitigation strategies, and outline open research questions. The result is both a blueprint and an agenda: a blueprint that shows how memory-augmented, tool-using LLM agents can be composed into robust recommendation pipelines, and an agenda inviting the RecSys community to develop benchmarks, theoretical guarantees, and governance tools that keep pace with this new degree of autonomy. By unifying agentic abstractions with recommender objectives, the paper lays the groundwork for the next generation of personalized, trustworthy, and context-rich recommendation services.