rsi-starter-loop-for-agent-systems

A safe recursive self-improvement (RSI) loop is a repeatable workflow that lets you measure agent behavior, hypothesize constraints, mutate workflows, and validate iterations without building a fully autonomous loop.

You can test agent system mutations by applying a concise starter workflow: measure current behavior, form a hypothesis about constraining factors, apply targeted mutations to prompts or routing, and optionally validate the change.

The RSI starter loop is designed for both individuals and teams who want a safe, repeatable workflow to measure and validate iterative improvements within agent ecosystems without requiring a full autonomous setup.

The best way to start measuring agent behavior is to use a structured starter loop that gathers signals from current performance, forms hypotheses about constraints, and applies controlled mutations to prompts or routing for testable validation.

No specific dependencies are required to run the RSI measurement loop. It operates using a SKILL.md file with YAML frontmatter and optional directories like scripts/ or references/ to supply runtime resources for the workflow.

A controlled RSI loop reduces friction in testing small iterations by providing a review-friendly workflow that safely measures and validates targeted mutations, whereas autonomous self-improvement lacks built-in validation gates for iterative changes.