A new approach to harness optimization for AI agents, called Mixture of Self-Improving Branches, is proposed. This method allows for adaptive improvement by organizing search into branches with evolving development subsets and proposal policies. The resulting complementary harnesses are shown to outperform a previous approach, Meta-Harness, on various benchmarks.