Policymakers must often act under conditions of deep uncertainty, such as emergency response, where predicting the specific impacts of a policy apriori is implausible. Large Language Model (LLM) agent simulations have been proposed as tools to support policymakers under these conditions, yet little is known about how such simulations become useful for real-world policy practice. To address this gap, we conducted a year-long, stakeholder-engaged design process with a university emergency preparedness team. Through iterative design cycles, we developed and refined an LLM agent simulation of a large-scale campus gathering, ultimately scaling to 13,000 agents that modeled crowd movement and communication under various emergency scenarios. Rather than producing predictive forecasts, these simulations supported policy practice by shaping volunteer training, evacuation procedures, and infrastructure planning. Analyzing these findings, we identify three design process implications for making LLM agent simulations that are useful for policy practice: start from verifiable scenarios to bootstrap trust, use preliminary simulations to elicit tacit domain knowledge, and treat simulation capabilities and policy implementation as co-evolving.