Google Research has open-sourced Harness² (or Harness2 ), a framework that lets AI agents improve themselves without retraining the underlying model. Instead of touching model weights, Harness² rewrites the harness around the model: its prompts, tools, memory, skills, and scripts. Then it carries what it learned into the next task.

The code ships under the Apache 2.0 license , alongside a research paper, Harness²: Recursive Agent Harnessing for an Open World , and a project site . The release is already being discussed on X .

This article explains how Harness² works and what its benchmark results show, then walks you through Google's free demo and a real benchmark run on Vertex AI. If you're looking for an open-source agent framework for self-improving AI agents, start here.

What Is Harness²?

A harness is everything around a model that lets it act as an agent: prompts, tools, memory, skills, and orchestration. Harnessing is the work of optimizing it.…