---
license: cc-by-4.0
language:
- en
size_categories:
- n<1K
task_categories:
- text-generation
- question-answering
tags:
- business-analysis
- requirements-engineering
- user-stories
- benchmark
- llm-evaluation
- agentic-rl
configs:
- config_name: default
  data_files: train.parquet
---

# BA-Agent-Bench (Subset)

**BA Agent Bench** — purpose-built benchmark for evaluating **LLMs and multi-agent systems on enterprise requirements generation capability**. Measures whether a model or agent can take real production work-item specs and produce BA-grade user stories with acceptance criteria, matching the decomposition and rigor of certified Business Analysts.

The full benchmark evaluates 8 systems (7 frontier LLMs + Centific BA Toolkit pipeline) on 7 enterprise features against 119 ground-truth user stories authored by certified Business Analysts. Composite score across **Alignment (35%) · Coherence (24%) · Completeness (18%) · Compliance (10%) · Testability (9%) · Spec Quality (4%)**.

This dataset card is an **8-feature stratified subset** of the benchmark — sampled for public release so practitioners can inspect inputs and gold standard.

Full benchmark, leaderboard, and harness → **[centific.com/benchmark/agentic-rl/1#start](https://www.centific.com/benchmark/agentic-rl/1#start)**.

---

## Why BA-Agent-Bench?

Existing BA / requirements-engineering benchmarks rely on toy specs or single-shot story generation. Real BA work has:
- Multi-document source specs (main requirement + KB / reference docs)
- Domain-rich decomposition: per-entity, per-workflow, per-stage stories
- BA-authored gold standard with full acceptance criteria
- 7 quality dimensions (alignment, coherence, completeness, testability, specification quality, trust, compliance)

BA-Agent-Bench targets all of the above on **production work items from an enterprise import/export logistics platform**.

---

## Subset Composition

This subset contains **8 features** covering a range of domains, document counts, and decomposition complexity:

| task_id | docs | stories | domain |
|---|---|---|---|
| T-001 | 4 | 27 | Customs Client ID processing |
| T-002 | 4 | 13 | Commencement configuration |
| T-003 | 4 | 12 | Brokerage Cargo Reports |
| T-004 | 4 | 17 | Cargo Reporting & Declaration |
| T-005 | 4 | 18 | TIN / OEM Client ID re-issue |
| T-006 | 4 | 12 | Reference File Processing |
| T-007 | 4 | 20 | OEM Client ID app improvement |
| T-008 | 3 | 8 | Shipment-level Underbond |

**Total:** 31 source documents · 127 golden stories · 8 distinct domains

---

## Schema

Each row has the following columns:

| Column | Type | Description |
|---|---|---|
| `task_id` | str | Original work-item identifier |
| `title` | str | Feature title |
| `description` | str | Feature description, scope, and business value |
| `input_documents` | list[dict] | Source requirement docs: `{filename, content}` |
| `golden_stories` | list[dict] | BA-authored user stories with acceptance criteria |

### `golden_stories` row

| Field | Type | Description |
|---|---|---|
| `story_id` | str | Story identifier within feature |
| `title` | str | Short story title |
| `description` | str | Full "As a / I want / So that" narrative |
| `acceptance_criteria` | str \| list[str] | Given/When/Then or free-text AC |
| `story_points` | int | Effort estimate |
| `state` | str | Workflow state when story was authored |

---

## How to Load

```python
from datasets import load_dataset

ds = load_dataset("CentificAIResearch/BA-Agent-Bench")
print(ds["train"][0])
```

Or read parquet directly:

```python
import pandas as pd

df = pd.read_parquet("hf://datasets/CentificAIResearch/BA-Agent-Bench/train.parquet")
print(df.columns.tolist())
for _, row in df.iterrows():
    print(row["task_id"], len(row["golden_stories"]))
```

---

## Full Dataset & Related Resources

This subset is drawn from a larger benchmark suite covering 26+ enterprise features with full evaluation infrastructure (LLM-judge metrics, multi-model leaderboard, automated scoring harness).

- **Benchmark portal:** [centific.com/benchmark/agentic-rl/1#start](https://www.centific.com/benchmark/agentic-rl/1#start)

---

## License

CC-BY-4.0 — free to use with attribution.

## Citation

```bibtex
@dataset{ba_agent_bench_2026,
  title     = {{BA-Agent-Bench}: Benchmark for {BA}-grade Story Generation and Multi-agent {BA} Pipelines},
  author    = {Centific AI Research},
  year      = {2026},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/datasets/CentificAIResearch/BA-Agent-Bench}
}
```