Agentic AI systems are autonomous AI systems that make decisions and take actions without constant human oversight. They enable autonomous agents that can cooperate, reason, and perform complex tasks (with little human involvement). Agentic AI systems have promising uses in high-stakes social applications like disaster response, where speed of decision, collaboration, and flexibility are keys. Current agentic AI tools like Phi Data, AutoGen, SmolAgents, LangGraph, and CrewAI vary widely in design and functionality and have not yet been extensively tested in such urgent real-world applications. This work presents a unified framework for deploying agentic AI in crisis management environments. It prioritizes speedy information synthesis, decision assistance, and multiparty inter-agent coordination. A comparative evaluation of the above systems is performed to analyze their applicability to real-time, high-stakes situations. We further advance CrisisGen, an extensible system that illustrates the operationalization of agentic AI for crisis response. Our research is directed towards informing the development of socially accountable AI systems that can drive better performance in high-stakes domains like emergency response, healthcare, environmental monitoring, and humanitarian relief.