Large language models have made remarkable strides in natural language processing, yet they still encounter difficulties when addressing complex planning and reasoning tasks. Traditional methods often rely on static templates or single-agent systems that fall short in capturing the subtleties of real-world problems. This shortfall is evident when models must verify generated plans, adapt to varying levels of complexity, or refine outputs iteratively. Whether it is scheduling meetings or solving scientific problems, the limitations of conventional approaches prompt the need for more nuanced and adaptable strategies.