---
pretty_name: UndoBench
license: cc-by-4.0
tags:
  - agentic-ai
  - llm-agents
  - ai-agents
  - agent-evaluation
  - recovery
  - reliability
  - fault-tolerance
  - tool-use
  - ai-safety
  - benchmark
configs:
  - config_name: default
    default: true
    data_files:
      - split: dev
        path: data/dev.parquet
      - split: validation
        path: data/validation.parquet
      - split: test
        path: data/test.parquet
---

# UndoBench

UndoBench is a benchmark for evaluating whether tool-using AI agents can recover safely from execution failures without duplicating, losing, or corrupting externally visible effects. The benchmark separates **task competence** from **recovery capability** using paired CONTROL and FAULT executions. It includes 36 enterprise workflows across 8 domains, with explicit fault scenarios and state/effect oracles for evaluation.

## Benchmark at a Glance

| | |
|---|---:|
| Workflows | 36 |
| Domains | 8 |
| DEV | 14 |
| Validation | 10 |
| Test | 12 |
| Benchmark version | v1.0.1 |

## Quickstart

```python
from datasets import load_dataset

ds = load_dataset("TanmaySah/undobench")

print(ds)
print(ds["test"][0])
```

## What Does One Row Represent?

Each row represents one canonical UndoBench workflow specification.

The 36 rows are benchmark task definitions, not individual model executions or aggregate experimental results.

Each workflow includes the task objective, available tools, fault metadata, environment/evaluation metadata, and provenance.

Full benchmark execution code and reproducibility artifacts are available in the GitHub repository.

## Schema

| Column | Type | Description |
|:---|:---|:---|
| `task_id` | `string` | Unique workflow identifier |
| `split` | `string` | `dev` / `validation` / `test` |
| `domain` | `string` | Enterprise domain |
| `workflow_name` | `string` | Canonical workflow title |
| `workflow_description` | `string` | Baseline workflow description |
| `objective` | `string` | Agent task instruction |
| `fault_scenario` | `string` (JSON) | Fault-injection specification |
| `fault_boundary` | `string` | Failure lifecycle boundary |
| `reversibility` | `string` | Action reversibility class |
| `idempotency_support` | `bool` | Native idempotency support |
| `tools` | `list[string]` | Available tools |
| `initial_state` | `string` (JSON) | Evaluator environment setup |
| `expected_outcome` | `string` (JSON) | Expected final-state assertions |
| `required_effects` | `string` (JSON) | Required semantic effects |
| `oracle_spec` | `string` (JSON) | Evaluation oracle |
| `benchmark_version` | `string` | Benchmark version (`v1.0.1`) |
| `source_path` | `string` | Canonical implementation path |

Evaluation note: initial_state, expected_outcome, required_effects, oracle_spec, and fault-injection metadata are published for reproducibility and are not provided to evaluated agents as privileged runtime inputs.

## Evaluation

The executable UndoBench evaluation harness is available in the [GitHub repository](https://github.com/tradertanmay/undobench).

Install and verify the benchmark:

```bash
git clone https://github.com/tradertanmay/undobench.git
cd undobench
pip install -e ".[all]"
undobench doctor
undobench smoke
```

Evaluate a custom agent on the validation split:

```bash
undobench run \
  --agent my_agent.py:MyAgent \
  --split validation \
  --recovery none \
  --seeds 42 \
  --output runs
```

Or evaluate an OpenAI-compatible model endpoint:

```bash
undobench run \
  --model openai/<model-name> \
  --split validation \
  --recovery naive \
  --seeds 42 \
  --output runs
```

After a run, recompute metrics and generate a standardized report:

```bash
undobench evaluate runs/<run_id>
undobench report runs/<run_id>
```

UndoBench reports task competence and recovery metrics including Control Pass Rate, RSR, CRSR, EOR, DER, and MER.

The frozen `test` split requires explicit opt-in before execution. See the [GitHub repository](https://github.com/tradertanmay/undobench) for full evaluation and integration instructions.

## Paper & Code

* [Paper (arXiv)](https://arxiv.org/abs/2610.05622)
* [Hugging Face Paper](https://huggingface.co/papers/2610.05622)
* [GitHub Repository](https://github.com/tradertanmay/undobench)

## Citation

```bibtex
@article{undobench2026,
  title={UndoBench: Separating Task Competence from Recovery Capability in Tool-Using AI Agents},
  author={Dolly Sah and Tanmay Sah and Harshul Jain and Tanya Sah},
  journal={arXiv preprint arXiv:2610.05622},
  year={2026},
  url={https://arxiv.org/abs/2610.05622}
}
```

## License

Dataset and benchmark specifications: **CC BY 4.0**

Software and execution framework: **Apache 2.0**