Google researchers release Harness2, which tunes an agent's setup task by task

Harness2, from a Google Research team and posted to arXiv on 9 October, improves the setup around an AI model, such as its prompts, tools, memory, skills and sub-agents, without changing the model itself. For each new task it tries a few edits, runs them, compares the results and keeps what worked, then saves the lessons for future tasks, all without needing an answer key. In the authors' tests with GLM-5 and Gemini 3.5 Flash, one round improved scores by up to 11.5 points on WorkBuddy-Bench, 4.5 points on JobBench and 11.8 percentage points on LAB. The code is open source under the Apache 2.0 licence, works with OpenCode and Codex, and is not an officially supported Google product.