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.

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

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. 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.

Choose one benchmark per command. WorkBuddy also takes --subset office, --subset web, or --subset code on every command after setup.

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.

After each task, harnessing-level recursion retains the adopted harness and evidence-supported lessons to guide future refinement.

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.

Results and resume

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.

Citation

@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}
}