This paper introduces Cross-Team Collaboration (CTC), a scalable multi-team framework that enables multiple teams to jointly propose diverse task-oriented decisions and communicate insights in a cross-team environment to generate high-quality content. The framework allows teams to collaborate effectively, exploring multiple potential decision paths within the solution space, which leads to superior content generation. Experimental results on software development and story generation demonstrate significant improvements in quality compared to state-of-the-art baselines, highlighting the effectiveness and generalization ability of the framework across various domains. The CTC framework consists of multiple teams working on the same task, with each team performing intra-team collaboration to divide the task into manageable subtasks. After key phases, teams communicate insights and use a greedy pruning mechanism to eliminate low-quality content, followed by collaborative aggregation to produce a superior outcome. This process enables teams to explore more potential paths and improve the quality of the generated content. The framework is evaluated on software generation tasks from the SRDD dataset and story generation tasks from the ROCStories dataset. Results show that CTC significantly improves software quality and story quality compared to single-agent and single-team approaches. The framework is also shown to be effective in handling complex tasks, including programming language generation and natural language generation. The study highlights the importance of diversity across teams and the benefits of cross-team collaboration in improving performance. The framework is designed to balance the quantity and quality of content, ensuring that the most valuable contributions are carried forward while minimizing communication burden. The results demonstrate the effectiveness of the CTC framework in enhancing software development and story generation tasks, and its potential for application in broader content generation domains.This paper introduces Cross-Team Collaboration (CTC), a scalable multi-team framework that enables multiple teams to jointly propose diverse task-oriented decisions and communicate insights in a cross-team environment to generate high-quality content. The framework allows teams to collaborate effectively, exploring multiple potential decision paths within the solution space, which leads to superior content generation. Experimental results on software development and story generation demonstrate significant improvements in quality compared to state-of-the-art baselines, highlighting the effectiveness and generalization ability of the framework across various domains. The CTC framework consists of multiple teams working on the same task, with each team performing intra-team collaboration to divide the task into manageable subtasks. After key phases, teams communicate insights and use a greedy pruning mechanism to eliminate low-quality content, followed by collaborative aggregation to produce a superior outcome. This process enables teams to explore more potential paths and improve the quality of the generated content. The framework is evaluated on software generation tasks from the SRDD dataset and story generation tasks from the ROCStories dataset. Results show that CTC significantly improves software quality and story quality compared to single-agent and single-team approaches. The framework is also shown to be effective in handling complex tasks, including programming language generation and natural language generation. The study highlights the importance of diversity across teams and the benefits of cross-team collaboration in improving performance. The framework is designed to balance the quantity and quality of content, ensuring that the most valuable contributions are carried forward while minimizing communication burden. The results demonstrate the effectiveness of the CTC framework in enhancing software development and story generation tasks, and its potential for application in broader content generation domains.