---
license: mit
language:
- en
pretty_name: Ambig-IaC
size_categories:
- n<1K
task_categories:
- text-generation
- question-answering
tags:
- infrastructure-as-code
- terraform
- code-generation
- ambiguity
- opa
- rego
---

# Ambig-IaC: Ambiguous Infrastructure-as-Code Benchmark

A benchmark dataset of **300 tasks** for testing AI agents that generate Infrastructure-as-Code (Terraform) configurations from ambiguous natural language intents.

**Project page**: [https://zyang37.github.io/ambig-iac.github.io/](https://zyang37.github.io/ambig-iac.github.io/)

## Dataset Description

This dataset is sourced from [IaC-Eval](https://github.com/autoiac-project/iac-eval). We performed manual fixes to the original Terraform configurations and validated that all 300 tasks pass `terraform plan`. Each task also includes the corresponding plan output in JSON format (`plan_json` field), which is easier to parse and compare programmatically than raw HCL.

Given an ambiguous infrastructure request (e.g., "I need a way to track which lookups are being made against our domain"), the agent is expected to generate a valid Terraform configuration. Each task includes the fully specified intent, a reference Terraform implementation, and OPA/Rego validation policies for evaluating the agent's output.

This dataset is useful for studying:
- How well AI agents generate correct IaC configurations from underspecified requirements
- Disambiguation strategies in IaC generation
- Iterative refinement of infrastructure configurations

## Fields

| Field | Type | Description |
|-------|------|-------------|
| `id` | int | Task index (0-299) |
| `prompt` | string | Ambiguous natural language requirement (~60 words avg) |
| `prompt_original` | string | Original detailed requirement with specific resource names |
| `intent` | string | Structured specification listing exact resources and attributes |
| `main_tf` | string | Reference Terraform (HCL) configuration |
| `checks_rego` | string | OPA/Conftest Rego policy for validation |
| `plan_json` | string | Terraform plan JSON output |

## Usage

```python
from datasets import load_dataset

ds = load_dataset("znyang/ambig-iac")

# Access a task
task = ds["train"][0]
print(task["prompt"])        # Ambiguous requirement
print(task["intent"])        # Detailed specification
print(task["main_tf"])       # Reference Terraform code
```

## Dataset Statistics

- **Tasks**: 300
- **Avg prompt length**: ~60 words
- **Avg intent length**: ~12 lines
- **Avg main.tf length**: ~98 lines
- **Avg checks.rego length**: ~69 lines
- **Domain**: AWS infrastructure (Route 53, CloudWatch, VPC, Kinesis, IAM, etc.)

## Citation

If you find this work useful, please cite:

```bibtex
@misc{yang2026ambigiacmultileveldisambiguationinteractive,
      title={Ambig-IaC: Multi-level Disambiguation for Interactive Cloud Infrastructure-as-Code Synthesis}, 
      author={Zhenning Yang and Kaden Gruizenga and Tongyuan Miao and Patrick Tser Jern Kon and Hui Guan and Ang Chen},
      year={2026},
      eprint={2604.02382},
      archivePrefix={arXiv},
      primaryClass={cs.SE},
      url={https://arxiv.org/abs/2604.02382}, 
}
```

## License

MIT