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Harness2 by Google Research – Recursive Agent Harnessing
tool
4 min read
Google Research has released Harness², an Apache 2.0-licensed framework for improving the software harness around fixed AI models rather than changing model weights. The framework edits eight agent components: system prompts, guardrails, memory, tool guidance, subagents, skills, scripts, and plugins. It uses a Propose–Probe–Compose process to test candidate harness edits on a task and create a refinement from differences in their behavior without ground-truth feedback. Harness² also retains adopted harnesses and evidence-supported lessons from completed tasks to inform later refinements. Parallel recursion evaluates multiple candidates more broadly, while sequential recursion carries a refined harness through successive steps. The project supports OpenCode and Codex executors and the JobBench, LAB, and WorkBuddy-Bench professional-task benchmarks. Google Research says one Harness² step improved GLM-5 and Gemini 3.5 Flash over base harnesses by up to 11.5 points on WorkBuddy-Bench, 4.5 points on JobBench, and 11.8 percentage points in LAB partial-pass rate; larger task-level recursion budgets produced further gains. The software requires Linux, Python 3.12, and uv, while model-backed runs also require model credentials and can incur API costs. Executor network access is unrestricted by default, and Codex runs with its sandbox disabled; users are warned to configure restrictions before evaluations because public benchmark answers or grading rubrics may be accessible online.