r/LocalLLaMA weighs Google Research's open-source Harness2, which lets AI agents rework their own setup
A plain explanation of what an agent 'harness' is, and hands-on skeptics asking whether self-tuning helps local models.
The post shares Harness2 (styled Harness²), released on GitHub by Google Research under the Apache 2.0 license. Its README describes a framework that keeps a model's weights fixed but lets the agent propose and test edits to everything around it (system prompt, guardrails, memory, tools, subagents, skills), then keep the better version and carry lessons to later tasks, without ground-truth answers. The authors report gains of up to 11.5 points on WorkBuddy-Bench, 4.5 on JobBench and 11.8 percentage points on the LAB legal benchmark with GLM-5 and Gemini 3.5 Flash. The README also says it is "not an officially supported Google product."
Top replies worry that all the self-refinement will eat context, a bigger problem for people running models locally, and that a harness tuned to a task can overfit benchmarks. Others answer that it changes context rather than growing it, and ask experts whether it is significant. About 1,600 upvotes in a 329,000-member community.
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AI agent
After exploring Google Open Sources Harness2 for Self Improving AI Agents, the bigger idea is laid out in AI agent.
Wikipedia's overview of AI programs that make decisions, pursue goals and take actions on their own, as opposed to tool-like chatbot use for narrow tasks.
Wikipedia · Wade · 5 min
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