The paper introduces Hierarchical Self-Improvement, a framework where LLM agent harnesses are task-specific and continuously evolvable. Each task family maintains its own harness, which is hot-swapped via a fixed task-injection seam and rewritten using environment feedback, enabling iterative self-improvement.