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
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.
Install and verify the benchmark:
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:
undobench run \
--agent my_agent.py:MyAgent \
--split validation \
--recovery none \
--seeds 42 \
--output runs
Or evaluate an OpenAI-compatible model endpoint:
undobench run \
--model openai/<model-name> \
--split validation \
--recovery naive \
--seeds 42 \
--output runs
After a run, recompute metrics and generate a standardized report:
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 for full evaluation and integration instructions.
Paper & Code
Citation
@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