Google Open-Sources Harness2, a Recursive Agent Harnessing Framework

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.

What is Harness²?

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.

What are the 8 harness components?

Harness² exposes 8 editable components, each mounted as a file:

System prompt

Guardrails

Memory

Tool guidance

Subagents

Skills

Scripts

Plugins (middleware)

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.

How does recursive agent harnessing work?

The framework runs two linked levels of recursion:

Task-level recursion uses a contrastive Propose, Probe, Compose step. First, the agent runs the task once with the base harness. An editor then proposes 2 candidates. The invariant candidate targets general agent failures such as planning or tool use. The domain candidate targets knowledge and procedures for the current domain. Both candidates run on the same task. The composer compares their trajectories against the reference and keeps only edits backed by observed behavior.

Harnessing-level recursion happens after each task. A reflector stores the adopted harness plus an EXPERIENCE.md file of lessons. This library guides future proposals. Each new task still starts from the base harness.

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.

How does Harness² perform on benchmarks?

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.

How does it compare with other harness methods?

*Scores as measured in the Harness² paper; the other methods did not report these benchmarks themselves.

How do you install and run Harness²?

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.

Check out the Paper, GitHub Repo, Paper and Project Page. All credit goes to the researcher of this project. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well.

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