Google Open-Sources Harness²: Self-Improving AI Agents Without Retraining

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. Today, most teams do it by hand: an engineer watches the agent fail, tweaks the prompt, adds a tool, and tries again. That works for tasks you planned for, but breaks down in what the paper calls an open world, where agents meet tasks nobody anticipated.

Harness² automates that tuning "while keeping model weights fixed" (README). There's no fine-tuning and no new training data; only the model's surroundings change. It can edit eight components:

Component	What it does in the agent
System prompt	The standing instructions the model reads first
Guardrails	Rules about what the agent must or must not do
Memory	Long-term notes carried across steps
Skills	Reusable instructions for specific kinds of work
Tool guidance	How and when to use each tool
Scripts	Helper code the agent can run
Subagents	Delegated helpers for parts of a task
Plugins	Middleware that shapes behavior inside the tool loop