autoresearch-agent

Automate code optimization by running autonomous experiment loops that edit target files, evaluate measurable metrics, and keep only improvements. This replaces manual trial-and-error by automatically measuring, evaluating, and compounding improvements to your code.

Yes, autonomous experimentation supports multi-domain optimization for marketing metrics like CTR and engagement scores. It automatically edits target content files, runs evaluations against your defined metrics, and commits only successful copy optimizations.

Setting up automated benchmark experiments requires a git-tracked repository, a target file for optimization, and a defined evaluation command that outputs a specific metric. Git manages changes by automatically rolling back failed experiments and committing successful ones.

The best way to run iterative experiments is a safety-first approach using git to manage changes. Every failed experiment is automatically rolled back, ensuring your repository remains stable while successful improvements are kept and compounded.

A git-tracked repository is required for automated prompt refinement to safely manage iterations. Git enables the agent to automatically roll back failed experiments that do not meet your metric goals while committing only successful prompt refinements.

The autoresearch agent handles failed optimization experiments by using a safety-first iteration approach with git. Failed experiments that do not meet your defined metric goals are automatically rolled back, preserving the last known good state of your target file.