Multi-Agent Framework for Personalized Learning Pathways

This document presents a technical report on a multi-agent architecture designed for adaptive educational recommendations. The framework addresses the unique challenges of educational personalization by employing over 18 specialized agents organized in a four-tier hierarchy. The first tier consists of perception agents that monitor learner behavior and generate insights. The second tier includes primary domain agents that provide expertise across various educational domains, such as programming and system design. Coordination agents in the third tier orchestrate the interaction between agents, ensuring coherent responses to learner queries. Finally, strategic agents in the fourth tier focus on long-term planning and personalized roadmaps. The report details the architectural principles, coordination protocols, and preliminary evidence from deployment on a learning platform with over 6,000 active users. The findings suggest that this hierarchical agent orchestration enables capabilities that surpass traditional single-model approaches, offering a promising paradigm for next-generation personalized learning systems.