ralph-loop

A bounded loop wraps your AI tool execution with per-iteration state logging and a verification contract to iterate until success is proven. It separates execution from verification to ensure repeatable, auditable outcomes.

Adding guardrails to autonomous workflows requires an execution and verification loop with explicit stop conditions and recovery paths. This pattern ensures safe HITL or AFK modes by validating progress against machine-verifiable acceptance criteria.

To verify progress during complex refactoring, you implement a loop that records per-iteration state and uses a verification script to check acceptance criteria. This provides positive proof of success and an auditable log of the entire process.

You can use a bounded iterative control loop for design reviews and complex data processing because it targets tasks with clear end states. It provides the explicit stop conditions and machine-verifiable acceptance criteria needed for these workflows.

You need explicit stop conditions and state logging when running AI tasks that require auditable recovery paths and positive proof of completion. This is essential for autonomous or HITL workflows handling complex refactors and data processing.