A groundbreaking development in Large Language Model (LLM) capabilities involves the proposal of a novel general agent framework for adaptive writing. This framework moves beyond simple text generation to more sophisticated, human-like cognitive processes by achieving adaptability through recursive task decomposition and the dynamic integration of three fundamental cognitive tasks: retrieval (information gathering), reasoning (content planning and logical inference), and composition (text generation). The core innovation lies in its ability to interleave task execution with planning, eliminating rigid, predefined workflows. Facilitated by a State-based Hierarchical Task Scheduling algorithm, tasks are executed at any planning node upon termination, allowing for immediate responsiveness to previous actions and their outcomes. This "heterogeneous recursive planning" enables LLMs to self-correct and strategically plan, tackling complex, multi-faceted creative and technical tasks with greater autonomy and coherence, signaling a new era for AI in content creation, where models can act as more sophisticated and iterative collaborators.