We present a scalable dual-agent architecture translating multi-source assessment data – integrating performance with process logs – into formative data insights. Decoupling classification from text generation, embedding expert rubrics, and optimizing latency enables rapid first-draft generation. These insights reveal underlying learning behaviors, facilitating targeted intervention without increasing teachers’ cognitive burden.