google-research / harness2

Recursive Agent Harnessing for an Open World

A harness is the system around a model that enables it to act as an agent: prompts, tools, memory, skills, orchestration, and more. Harnessing is the process of optimizing that system.

Harness² is a plug-and-play framework for recursive agent harnessing in an open world, where agents encounter tasks unseen during harness design. It connects two levels of recursion while keeping model weights fixed:

• Task-level recursion refines the execution harness for the current task through a contrastive Propose–Probe–Compose step: propose edits, probe their effects on the same task, and compose a refined harness from behavioral contrasts between executions, without ground-truth feedback.
• Harnessing-level recursion updates a persistent improvement harness with adopted harnesses and evidence-supported lessons from each task to guide future refinement.

Across three professional benchmarks and four model–harness configurations, a single Harness² step improves GLM-5 and Gemini 3.5 Flash over their base harnesses, with gains of up to 11.5 points on WorkBuddy-Bench, 4.5 points on JobBench, and 11.8 percentage points on LAB's partial pass rate. Increasing the task-level recursion budget brings further gains.

The release supports parallel and sequential recursion, OpenCode and Codex executors, and JobBench, LAB, and WorkBuddy-Bench.

Video overview

https://github.com/user-attachments/assets/76b10548-b14b-4c8f-9aa5-a93662df4d56

Contents

• Video overview
• Harness components
• Install
• Run a benchmark
• Experiments
• Web access
• Results and resume
• Citation
• Contributing
• License
• Disclaimer

Harness components

Harness² exposes eight editable components around the model: system prompt, guardrails, memory, tool guidance, subagents, skills, scripts, and plugins. They control context construction, the tool interface, delegation, and behavior inside the tool loop.

Install

Use Linux, Python 3.12, and uv. From the repository root:

uv venv --python 3.12 .venv
source .venv/bin/activate
uv pip install -e '.[render]'
harness2-demo --output runs/demo --k 2

The local demo runs both recursion modes with deterministic editors and real Python executions. It requires no model credentials.

For model-backed benchmarks, install Node.js 20+, the Google Cloud CLI, and the benchmark dependencies. LAB needs Pandoc and Poppler; WorkBuddy-Bench needs Docker. Then configure your models:

cp .env.example .env
# Edit .env with your Vertex AI project and model IDs.
gcloud auth application-default login

See the setup guide for model settings, authentication alternatives, and the Codex executor.

Run a benchmark

The following example installs LAB and runs one task, then evaluates and reports its result. Model-backed runs incur API costs; start with a small task selection.

harness2-setup lab
harness2-check --bench lab
harness2 --bench lab --k 1 --mode parallel \
  --only-domains antitrust-competition --max-tasks 1
harness2-judge --bench lab --k 1 --mode parallel
harness2-report --bench lab --k 1 --mode parallel

Sequential recursion runs the same commands with --mode sequential. --k is the task-level recursion budget; see Experiments. Judging and reporting must use the same benchmark, executor, subset, --k, --mode, and seed as the refinement run. Add --dry-run to harness2 to preview task selection without model calls.

Benchmark	Tasks	Install	Guide
JobBench	Professional work	harness2-setup jb	JobBench
LAB	Legal work	harness2-setup lab	LAB
WorkBuddy-Bench	Office, web, and code	harness2-setup wb	WorkBuddy

Choose one benchmark per command. WorkBuddy also takes --subset office, --subset web, or --subset code on every command after setup. The benchmark guides cover each one's data, domains, and grading.

Experiments

Scale task-level recursion in two ways: parallel recursion explores multiple candidate harnesses and composes refinements from their execution evidence; sequential recursion carries the refined harness and accumulated evidence through successive Propose–Probe–Compose steps.

Parallel recursion explores wider; sequential recursion refines deeper.

Option	Behavior
--k 1	One Propose–Probe–Compose step
--k K --mode parallel	Probe K general and K domain-specific candidates, then produce K compositions
--k K --mode sequential	Refine the harness through K successive steps
--substrate cx	Use Codex instead of the default OpenCode executor

After each task, harnessing-level recursion retains the adopted harness and evidence-supported lessons to guide future refinement. See the experiment guide for task selection, concurrency, reproducibility, and baseline comparisons.

Web access

Executor network access is unrestricted, and Codex runs with its sandbox disabled. Public benchmark rubrics may be accessible online. Before evaluation, configure network restrictions to prevent access to benchmark answers and grading criteria.

Res