Google Open-Sources Harness2, a Recursive Agent Harnessing Framework - MarkTechPost
Google Cloud AI Research has open-sourced Harness². Harness² is a plug-and-play framework from Google Research that refines an agent’s harness during deployment. Source: Harness² paper, Tables 1 and 2.
Google Cloud AI Research has open-sourced Harness². It is a recursive agent harnessing framework that improves the code and files around a frozen model. For developers, the pitch is simple. Your agent may get better without a better model.
Harness² is a plug-and-play framework from Google Research that refines an agent’s harness during deployment. A harness is everything around the model: prompts, tools, memory, skills and orchestration. Harnessing is the process of optimizing that system. Model weights never change.
The target setting is the open world. Tasks arrive unseen, and no verifier says whether an answer is right. The research team argues that most self-improvement methods assume labels or test cases. Harness² is built to work without them.
Harness² exposes 8 editable components, each mounted as a file:
They cover context construction, the tool interface, delegation and behavior inside the tool loop. The layout extends the 7-file split from earlier Agentic Harness Engineering work by 1 component.
The framework runs two linked levels of recursion:
The budget k scales the search. Parallel mode probes k invariant and k domain candidates, then makes k compositions. Sequential mode chains k refinement steps.
The research team tested 3 professional benchmarks: WorkBuddy-Bench, JobBench and Harvey’s LAB. Executors were Gemini 3.5 Flash and GLM-5 under OpenCode and Codex. The default editor was Claude Opus 4.8. Results are means over 3 runs.
Source: Harness² paper, Tables 1 and 2.
Weaker executors gained more. After 1 step, GLM-5 beat base Gemini 3.5 Flash on WorkBuddy-Bench. Parallel recursion at k=5 lifted Gemini under OpenCode to 80.8 on WorkBuddy. On WorkBuddy-Bench, 3 of 4 setups also used fewer steps per task. Gemini under OpenCode fell from 25.4 to 21.9.
*Scores as measured in the Harness² paper; the other methods did not report these benchmarks themselves.
The README targets Linux, Python 3.12 and uv. A local demo runs both recursion modes without model credentials:
Model-backed runs need Node.js 20+, the Google Cloud CLI and a Vertex AI project. LAB needs Pandoc and Poppler. WorkBuddy-Bench needs Docker. Executors are OpenCode by default, or Codex with --substrate cx.
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