autoresearch

To automate autonomous AI experiments, configure a baseline command, metric extraction, and evaluation protocol on a dedicated branch. The agent then continuously proposes, implements, runs experiments, and logs outcomes without constant human prompting to achieve repeatable research.

Branch-based version control for autonomous workflows commits every code change before execution on a dedicated branch. This preserves history and enables rollback, ensuring iterative development remains safe and repeatable across experiments.

Guard policies and recovery prompts manage failures and avoid regressions during AI agent benchmarking. You configure a guard mechanism alongside the baseline command to evaluate execution outcomes and automatically trigger recovery protocols when needed.

Yes, you can use autonomous experimentation loops for testing model configurations. The workflow applies across codebases requiring baselines and metrics, running a perpetual cycle of edit, commit, run, measure, and keep or revert.

The best way to log metrics and gather reproducible results is applying a version-controlled iteration loop. Every experiment is committed before execution, preserving version history while the agent continuously logs outcomes against a configurable baseline.