Mar 2026
CR8: Curriculum-to-Content Agent Pipeline
- Private, walkthrough on request
This work is private. A walkthrough is available on request.
Built a 3-agent LangGraph pipeline (Ingest → Research → Generate) that turns raw university curriculum into a complete learning package: structured study guides, gap-analysis decks, video scripts, AI-narrated video, and a Bloom's-taxonomy MCQ quiz engine. A two-layer evaluation framework gates every output, and severity-based model routing keeps generation cost near zero without dropping quality where it matters.
Problem
Turning raw curriculum into structured learning content is manual, slow, and inconsistent — and without evaluation, generated educational material can't be trusted for real use.
Approach
3-agent LangGraph pipeline (Ingest → Research → Generate) producing study guides, gap-analysis decks, video scripts, AI-narrated video via Kokoro TTS, and Bloom's-taxonomy MCQ quizzes. A two-layer evaluation framework: free structural checks on every output, plus a DeepSeek-V3 LLM judge at roughly $0.02 per run, with severity-based routing across GPT-5-nano/mini/5.1.
Result
End-to-end curriculum-to-content system in production on Cloud Run — guides, decks, narrated video and assessment generated from a syllabus, with every output scored before it ships. 1,299 tests across pytest, Vitest and Playwright E2E, with zero real API calls in the suite.