MACRS Multi-Agent Act Planning Framework

In the previous lesson, we saw how MACRS reimagines conversational recommendation as a multi-agent system, where different agents bring specialized capabilities to the table. But here’s a key challenge: even with well-designed agents, who decides what actually gets said?

Imagine this everyday moment: you’re chatting with a recommendation assistant about what to watch tonight. One moment, it asks if you like thrillers. Next, it throws a random movie at you. Then it loops back to “So, what genre do you enjoy?” It feels scattered, even though each individual response may sound fine.

This is the problem with act planning. It’s not about what can be said, but about what should be said next to move the conversation forward. In goal-directed dialogue, the system isn’t just filling time; it’s trying to accomplish something. That means every turn should be:

• Purposeful

• Context-aware

• Strategically chosen

Many LLM-based chat systems fall short here. They generate plausible-sounding replies, but without a clear planning mechanism, they often stall or meander. MACRS takes a different route. It breaks the “what to say” problem into two coordinated steps:

1. Generate options using specialized responder agents.

2. Select the best one using a central planner agent.

This cooperative act planning pattern is a prime example of the Manager-Worker orchestration model we discussed in Chapter 1, explicitly designed for goal-directed dialogue in CRSs.

Generating response options

Instead of relying on a single model to juggle all conversational tasks, ...