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.
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
from datasets import load_dataset
ds = load_dataset("CentificAIResearch/BA-Agent-Bench")
print(ds["train"][0])
Or read parquet directly:
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
License
CC-BY-4.0 — free to use with attribution.
Citation
@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}
}