The authors propose VACE, a method for co-evolving agent models and their harnesses through alternating reinforcement learning and trajectory-driven refinement. This approach is shown to improve performance on OfficeQA and AutomationBench benchmarks, outperforming weight-only reinforcement learning and ungated alternation.