Building a Multi-Agent AI Orchestrator: Lessons from CourseSmith

When I set out to build CourseSmith, I had a simple thesis: if AI can generate code, it should be able to generate structured educational content. The reality was far more complex — and far more interesting.

The Problem with Single-LLM Content Generation

Early experiments with single-prompt course generation produced shallow results. A single LLM call, no matter how well-crafted the prompt, lacks the multi-perspective depth that quality educational content requires. A course needs a curriculum designer’s structure, a subject matter expert’s depth, an assessment designer’s rigor, and a publisher’s polish.

That’s when I shifted to a multi-agent architecture.

The Architecture

CourseSmith uses 4 specialized AI agents that collaborate through a YAML pipeline:

• Teacher Agent — designs the curriculum structure, modules, and learning objectives
• Quiz Generator — creates assessments with varying difficulty levels and knowledge checks
• Flashcard Generator — extracts key concepts and creates spaced-repetition flashcards
• Publisher Agent — formats everything into production-ready Markdown output

Each agent operates independently but feeds into the next stage, with quality gates at each handoff. The result is content that feels designed, not generated.

Key Lessons

1. Pipeline > Monolith — Breaking generation into stages with clear interfaces makes each agent’s job simpler and the output more consistent.
2. Plugin Architecture Matters — CourseSmith’s runtime is agnostic to the LLM provider. The same pipeline works with OpenAI, Claude, or any compatible API.
3. Quality Gates Prevent Garbage — Each agent validates the previous stage’s output before proceeding, preventing cascading errors.

The full CourseSmith project generated a complete 20-module Docker course as a proof point — from syllabus to final assessments — in hours instead of weeks.

Built with: TypeScript, Node.js, OpenAI, Claude, Plugin Architecture