Achieving expert-level performance through simulation-based training relies heavily on complex and adaptable scenarios, but creating these scenarios manually is often laborious and resource-intensive. Large Language Models (LLMs) offer a promising avenue to automate and enhance scenario generation. However, their reliance on purely sequential text generation in standard prompting settings can hinder consistent understanding in complex systems. We present a multi-agent reasoning framework to leverage LLMs for puzzle generation within the 2D Physics Puzzle Environment CREATE (Chain REAction Tool Environment) to overcome these limitations. This testbed is used as a simplified analogy for scenario generation to allow the development of fundamental LLM capabilities needed to collaboratively design and solve intricate challenges, with a long-term goal of application in domains such as military training. Our framework employs a multi-agent ReAct architecture, integrating reasoning and action feedback loops to dynamically interact with CREATE. By assigning distinct roles, such as solver and designer, to individual agents, our framework preserves the complex reasoning pathways required for solving and generating puzzles—enabling complex reasoning that was too difficult to achieve with basic prompting or single-agent approaches. This work represents a step towards more robust LLM-driven scenario generation by demonstrating the ability of a multi-agent system built on our framework, while interacting with CREATE simulations, to collaboratively perform multi-step reasoning and adapt to environmental constraints. While not yet achieving real-world scenario generation, our findings demonstrate the potential of LLMs to generate solvable puzzles aligned with user prompts. However, we also highlight and address persistent challenges with their reasoning about precise spatial relationships and understanding complex, multi-step chain reactions, which are crucial for generating more advanced scenarios. We conclude by discussing the future role of multi-agent LLM frameworks in creating realistic and adaptable training scenarios for various applications, building upon the foundational capabilities developed in this work. Examples and our full code are available at: https://github.com/binzeli/Puzzle_Generation .