HACE enables joint optimization of specialized foundation models and LLM agents through reciprocal learning. The framework uses supervised learning to improve the specialist model while simultaneously training the agent via on-policy reinforced self-training based on verified performance gains. This approach addresses the drift between evolving domain experts and their controlling agents.