Recently discovered an interesting approach that wasn't in my original blueprint when building a multi-agent framework for automated literature review across different domains. The architecture I'm exploring now keeps research agents running in parallel streams, which significantly improves efficiency. Then a study evaluator component grades and ranks these sources using a standardized scoring script—this prevents bias and ensures consistency. Finally, a synthesis agent consolidates all the evaluated findings into coherent insights. The parallel processing architecture is pretty elegant for handling large-scale research tasks in Web3 and blockchain domains where information volume is massive.