AI agents are increasingly adopted in higher education, yet current systems handle requests uniformly, promoting cognitive offloading over sustained skill development. With the rise of orchestrated multi-agent systems, learning goals can be targeted by specialized AI agents that each address a distinct scaffolding mechanism: conceptual, procedural, strategic, or metacognitive. This study follows a Design Science Research approach to derive design requirements from 32 student interviews, iteratively refine a prototype with 22 IS experts, 6 educators, and 34 students, and computationally analyze scaffold effectiveness. We contribute three design principles: (1) multi-agent coordination through a lead orchestrator that delegates to mechanism-specific sub-agents, (2) adaptive learner profiling that enables cross-session scaffolding fading, and (3) continuous institutional knowledge integration grounding scaffolds in verified course materials. Our effectiveness analysis reveals that delivery order predicted learning behavior, positioning orchestration as a key design concern for AI-assisted educational systems.