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Agentic System Design Crash Course

Designing a Multi-Agent Conversational Recommender System

Explore how to design a multi-agent conversational recommender system that uses specialized agents and a planning framework to guide natural, goal-directed dialogue. Understand how feedback-aware reflection mechanisms adapt recommendations based on user behavior and improve dialogue flow. This lesson equips you to build systems that balance engagement, relevance, and efficiency in multi-turn conversations.

We'll cover the following...

Recommender systems play a central role in personalized digital experiences. Whether we’re choosing a movie, a product, or a restaurant, recommendations shape our decisions, often without us realizing it. But as user expectations evolve, traditional recommendation interfaces (sliders, forms, static suggestions) are being replaced with conversational recommenders. This is where users interact with an AI assistant via natural dialogue. In this lesson, we will design a multi-agent conversational recommender system for goal-directed dialogue.

The design challenge and goals

Imagine you’re chatting with a Smart Assistant. What would make that conversation helpful? Most likely, it needs to:

Design goals of a conversation recommender system in the era of agentic AI
Design goals of a conversation recommender system in the era of agentic AI

  1. Switch conversation styles: Sometimes the system needs to ask questions (“Do you like action movies?”), sometimes recommend something (“You might enjoy Inception”), and sometimes just keep the chat friendly (“That’s a great choice!”). A good assistant knows how to blend all three naturally.

  2. Learn from behavior: Even if you don’t say “I don’t like horror,” skipping scary movie suggestions sends a signal. The recommendation assistant should pick up on that and adjust its strategy.

  3. Decide what to say: One part of the system might want to ask more questions, another might be ready to recommend. The recommendation assistant has to choose the smartest next move.

  4. Remember what matters: If you said you like comedy earlier or searched for romantic movies last week, the system should keep that in mind during the conversation.

  5. Keep the conversation on track: The system isn’t just here to chat. It’s trying to help you find something you’ll actually choose. It needs to guide the conversation toward that goal, step-by-step.

These are the real-world challenges MACRS is built to handle. Next, we will learn how its design, built around multiple specialized ...