Can AI agents adapt zero-shot, to complex multi-step language instructions in open-ended environments? We present MaestroMotif, a method for AI-assisted skill design that produces highly capable and steerable hierarchical agents. To the best of our knowledge, it is the first method that, without expert labeled datasets, solves compositional tasks requiring hundreds of steps for completion. All the modules within MaestroMotif are learned from interaction: from the highest level of planning to the lowest-level of sensorimotor control. On the open-ended domain of NetHack, it surpasses existing approaches, including those that are fine-tuned specifically for each task. At the heart of MaestroMotif is the idea that decomposing a task into subtasks significantly helps decision making. MaestroMotif leverages an agent designer's intuition about a domain to identify important skills and describe them in natural language. These short descriptions then get converted into adaptable hierarchical agents through AI feedback and in-context learning. Our paper was recently published at ICLR 2025 and we open-source the whole project including the code, prompts and pre-trained models. Paper: arxiv.org/abs/2412.08542 Code: github.com/mklissa/maestromo… NotebookLM Podcast: bit.ly/4jLi6mo This work was done with the amazing @HenaffMikael, @robertarail, @shagunsodhani, Pascal Vincent, @yayitsamyzhang, @pierrelux, Doina Precup, with equal supervision by @MarlosCMachado and @proceduralia. Take a look at the following thread: