Addressing the decision-making conundrum faced by commanders in response to major sudden incidents， this paper proposes a coordination framework for collaborative disposal of multi-intelligent agents based on large language models. The framework optimizes collective decision-making efficiency and action planning through strategies such as agent role generation， multi-level Monte-Carlo tree and interactive prompt learning. It introduces hierarchical mechanisms and workflow management concepts， enhancing collaboration efficiency through the reward function shared among agents. A transparent and implicit communication model ensures node status consistency. Experimental results demonstrate that the framework performs well under various scenarios， significantly improving reaction speed and response efficiency compared to traditional task allocation methods.