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Automate metric-driven code quality improvements by running single-change experiments to optimize a defined goal. The iterative loop applies guardrails and an auditable log to track progress, validating each iteration against a baseline before committing.

An iterative metric-driven optimization loop is a process that turns vague improvement goals into structured, repeatable experiments. It performs one-change iterations to move toward a target metric, such as test coverage or performance, while logging baseline and iteration results.

Track and revert code changes during metric optimization using an auditable experiment log. The loop records baseline and iteration results, applying guard validation to ensure progress and enabling reversion when a single-change experiment fails to improve the target metric.

You can use single-change experiments to optimize test coverage, performance, or code quality in any workspace where a goal is defined and progress is measurable. The loop applies guardrails to ensure each iteration safely moves toward the target metric.

You need a defined goal file to start automating metric improvements. The process requires a GOAL.md definition to establish the target metric, along with guard validation, ensuring the automated experiment loop has a clear objective and safety constraints.

Automated experimentation loops for code optimization are limited to workspaces where progress can be clearly measured by defined metrics. Without a measurable goal, guard validation, or an auditable log to track baseline results, the single-change iteration process cannot function effectively.