LLM-based Multi-agent frameworks have shown a great potential in solving real-world software development tasks, where the agents of different roles can communicate much more efficiently than humans. Despite their efficiency, LLM-based agents can hardly fully understand each other, which frequently causes errors during the development process. Moreover, the accumulation of errors could easily lead to the failure of the whole project. In order to reduce such errors, we introduce an intention aligned multi-agent framework RTADev, which utilizes a self-correction mechanism to ensure that all agents work based on a consensus. RTADev mimics human teams where individuals are free to start meetings anytime for reaching agreement. Specifically, RTADev integrates an alignment checking phase and a conditional ad hoc group review phase, so that the errors can be effectively reduced with minimum agent communications. Our experiments on various software development tasks show that RTADev significantly improves the quality of generated software code in terms of executability, structural and functional completeness. The code of our project is available at https://github.com/codeagent-rl/RTADev.