Multi-Agent Conversational Recommendation Systems with MACRS

Modern recommendation systems are evolving past simple search algorithms to become dynamic, conversational partners. Building these complex systems requires a structured approach to managing multiple specialized AI agents. This course provides a clear, step-by-step path to understanding and implementing the Multi-Agent Conversational Recommendation System (MACRS) framework. You will start by exploring core agentic concepts, understanding how specialized roles collaborate, and learning how to maintain goal-driven coherence in conversational interfaces. What you'll learn: - Understand the fundamental architecture of multi-agent systems and conversational recommendation. - Configure specialized agent roles to handle distinct phases of user interaction. - Implement structured planning techniques to keep conversational agents on track. - Apply prompt engineering and context management to maintain dialogue coherence. - Practice designing evaluation metrics to measure recommendation accuracy and user satisfaction. The curriculum begins with foundational definitions of multi-agent orchestration and conversational AI before guiding you through the systematic design of a MACRS-based workflow. This course is designed for software developers, AI enthusiasts, and system architects who are new to multi-agent frameworks and want to build structured, conversation-driven recommendation engines. Start your journey into advanced multi-agent planning today.