The dominant paradigm for teaching autonomous language‑model agents is to let each instance wander through its own training episodes, rediscovering the same sub‑tasks over and over. That redundancy inflates exploration budgets and leaves even modest models struggling on long‑horizon problems. A fully automated pipeline that extracts reusable, hierarchical behaviors from a collective pool of trajectories flips the script. Historically, agents have relied on flat replay buffers or hand‑crafted macro‑actions; neither approach captures the layered structure of real‑world plans. Without an explicit representation that separates strategy, function, and atomic operation, weaker backbones cannot efficiently retrieve the right piece of experience when a new request arrives. This limitation has kept them a step behind larger, compute‑heavy models.…