This thesis investigates the practical implications of Large Language Models (LLMs) in the design and implementation of Multi-Agent Systems (MAS), with special attention to their integration into Recommender Systems (RecSys). Specifically, the focus is on the design, development, and evaluation of a Multi-Agent RecSys architecture built upon three core principles: reusability (implementable in different use cases and domains), scalability (ready to serve a large audience), and explainability (transparency at every step). At the core of the pipeline, orchestration policies ensure the asynchronous scheduling of agent tasks across modules: data gathering, data extraction, user profiling, and recommendation generation. LLM instruction prompting adopts a schema-driven (JSON) approach to produce observable and debuggable outputs.