autoresearch-agent

Automate metric-driven code optimization loops by configuring an agent to edit a target file, run a fixed evaluation, and commit metric improvements via git while discarding unsuccessful changes with resets.

An autonomous experiment loop for measurable improvement is an automated workflow that iteratively edits a file, evaluates metric output, and records outcomes in a git-backed history for comparative analysis.

Yes, you need a git repository to run automated evaluation workflows because the agent uses git commits and resets to track successful changes and discard unsuccessful iterations for reproducibility.

You can use automated experimentation for optimizing prompts and content because the workflow applies to any measurable improvement scenario where a reproducible git-backed target file and evaluation command exist.

The best way to track code optimization metrics over time is using a git-backed history that records commits and evaluation outcomes, enabling reproducibility and comparative analysis of automated experiment loops.

You should avoid using automated metric optimization loops when your project lacks a reproducible git-backed workflow, a specific target file, or a fixed evaluation command that outputs a measurable metric.