In our work, we leverage advancements in agentic Large Language Models (LLMs) to enhance their skill decomposition and autonomous learning capabilities. We introduce a freeform action space, prompting the LLM to decompose complex problems into simpler tasks. Our hypothesis is that this approach will enable the LLM to incrementally build its skill set and strategically chain actions for problem-solving. Furthermore, we propose a novel retrieval-augmented learning framework, encouraging the model to acquire new skills from diverse prompts. This framework aims to enrich the LLM’s knowledge base, allowing it to apply newly learned skills to solve the designated problem set effectively.