In this work, we present a framework for handling multiple, simultaneous, natural language queries, in a resource constrained environment, using Quality of Information (QoI). Incoming queries are first parsed into response graphs, tree-like structures designed to formalize a system's understanding of user intent, via a pragmatics toolkit. The system then uses a combination of QoI-awareness, adaptive intent determination, and packing algorithms to maximize the QoI realized by the system. We employ two different methods of evaluation, a one-shot model and an iterative, time-staged model. Under the one-shot model, packed jobs are answered and the rest discarded, and we aim to maximize the total realized QoI. Under the staged model, the system repeatedly packs and offers answers until all jobs are complete; here we aim to maximize time-weighted QoI and minimize completion time. We evaluate the performance of different instantiations of our system through thousands of procedurally-generated simulations.