RRSI is a method for automatically improving LLM agent systems by iteratively refining prompts, control flow, and tooling while preventing overfitting through regularization constraints. The approach uses a budget-limited proposer and a critic-pruner selector to favor reusable mechanisms, achieving significant gains on in-distribution and out-of-distribution benchmarks while reducing computational cost.