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Automate iterative code generation by running an autonomous loop that continuously refines a defined objective through modify, verify, and keep or discard cycles. The loop evaluates mechanical verification metrics to guide each subsequent iteration automatically.

Autonomous goal-directed iteration is a looping process that continuously improves content optimization tasks by applying one change at a time. It uses measurable criteria to evaluate modifications, keeping improvements and discarding failures until termination conditions are met.

You can use autonomous loops for design exploration within bounded constraints. The loop applies safety guidance and rollback mechanisms to ensure each experimental change is mechanically verified before being kept or discarded in the persistent results log.

Start an autonomous looping task by invoking the loop on a defined objective. The system automatically handles decision-making based on measurable criteria, executing one change at a time and logging persistent results to guide next iterations without manual intervention.

Autonomous iteration works without external dependencies. The loop operates independently by relying on its internal mechanical verification logic, constraint-driven safety guidance, and a persistent results log to drive the modify, verify, and keep or discard workflow.

Constraint-driven iteration is limited by the need for clear metrics and mechanical verification. Without measurable criteria and bounded constraints, the autonomous loop cannot effectively evaluate modifications, make decisions, or guide the next iterations for code, content, or data tasks.