agentic-loop

Automated iterative code improvement requires setting a goal, evaluation command, target threshold, and max iterations to execute hypothesis-driven edits. The system commits or discards small reversible changes based on baseline tracking and measurable metric progress.

Metric-driven workflow automation is a process that coordinates iterative improvement cycles for software artifacts using a single governing metric. It applies controlled development tasks with baselines, state tracking, and branching for experimentation to safely evolve code.

To set up an iterative improvement loop, you provide a goal, evaluation command, metric, threshold, and maximum iterations. This configuration enables the system to apply structured logging for automated decision making and escalation across development cycles.

Yes, you can resume automated code improvement sessions across iterations using built-in escalation and resume rules. The system supports structured logging and state tracking to manage pivots, restarts, and session resumption for metric-driven development tasks.

Use a metric-guided loop for code changes when you have a controlled development task with a single measurable metric governing progress. It is ideal for applying small, reversible edits based on explicit evaluation commands and defined thresholds.

Metric-driven iterative improvement is limited to controlled development tasks where a single metric governs progress. It requires explicit evaluation commands, a defined threshold, and structured logging, making it unsuitable for multi-objective optimization or unmeasurable code changes.