autoresearch-agent

You can automate file optimization by running an autonomous experiment loop that edits a single target file, evaluates changes with a fixed numeric metric, and iterates toward improvement while handling git-based commits and rollbacks.

Autonomous experimentation is an iterative process where an agent edits a target file, evaluates the change using a deterministic metric, and either keeps or discards the modification to achieve measurable improvements.

Yes, you need a git repository containing the target file to run automated optimization experiments. The system relies on git to commit successful changes and roll back unsuccessful iterations during the evaluation loop.

To set up an automated experiment loop, you need a git repository with your target file, an evaluation command that outputs a numeric metric, a .autoresearch configuration file, and an initial results history to track progress.

Yes, iterative optimization techniques apply to documentation and prompts across engineering, content, and marketing contexts, using measurable metrics to evaluate and keep or discard single-file changes.

Autonomous file optimization loops are limited to editing one target file per iteration to isolate the effect of each change, requiring a deterministic evaluation command that outputs a numeric metric to function correctly.