Harness²
Harness² (from Google Research and Northwestern) is a framework for improving a frozen, non-retrained agent by rewriting the harness around it instead of fine-tuning the model's weights. It runs two nested recursion loops: a task-level loop that proposes, probes, and composes candidate edits to the harness for the task at hand, and a harnessing-level loop that distills lessons from those episodes into a continually-improving harness carried forward into future tasks.
Read the brief