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.
harness2-video.mp4
- Video overview
- Harness components
- Install
- Run a benchmark
- Experiments
- Web access
- Results and resume
- Citation
- Contributing
- License
- Disclaimer
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.
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.
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.
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.
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.
Results are saved under runs/
, organized by benchmark and subset where
applicable. They include task summaries, deliverables, adopted harnesses,
reflection notes, and evaluation results.
Repeat the same command to resume. Use a new HARNESS2_RUNS_DIR
when changing
models or task selection. See results and resume
for details; each command also supports --help
.
@article{xu2026harness2,
title = {Harness$^2$: Recursive Agent Harnessing for an Open World},
author = {Xu, Ruiyao and Chen, Yanfei and CuiZhu, Zhongying and Dalvi Mishra, Bhavana and Ming, Yifei and Yu, Han and Han, Rujun and Lee, Chen-Yu and Pfister, Tomas},
journal = {arXiv preprint arXiv:2610.14224},
year = {2026},
url = {https://arxiv.org/abs/2610.14224}
}
See CONTRIBUTING.md.
Harness² is licensed under Apache 2.0. Third-party code under
third_party/
, benchmarks, and datasets retain their
upstream licenses; see
third-party notices.
This is not an officially supported Google product. This project is not eligible for the Google Open Source Software Vulnerability Rewards Program.