This paper introduces an autoresearch framework that uses AI coding agents to optimize ground-state preparation protocols for quantum and quantum-classical many-body systems. It demonstrates how this method can effectively tune hyperparameters for VQE, DMRG, and AFQMC protocols, leading to improved energy proxies within fixed computational budgets across various spin models and molecular Hamiltonians.