ibm-research/BPO-Bench

BPO Benchmark Dataset

Evaluation dataset for AI agents using recruiting analytics APIs. This benchmark tests an agent's ability to use tool APIs to answer questions about BPO (Business Process Outsourcing) recruiting data.

Dataset Structure

Files

• candidate_data.parquet (1.8 MB): 64k synthetic candidate records with recruiting funnel data
• candidate_data.csv (13.5 MB): Same data in CSV format for human inspection
• tasks.json (26 KB): 26 core evaluation tasks with ground truth
• tasks_type_mismatch.json: 3 tasks testing agent handling of unexpected data types
• tasks_http_errors.json: 4 tasks testing agent handling of HTTP error codes
• tasks_schema_violations.json: 4 tasks testing agent handling of schema violations
• tasks_edge_cases.json: 5 tasks testing agent handling of edge cases (large payloads, Unicode, deep nesting)
• tasks_undocumented.json: 3 tasks testing agent handling of undocumented API behaviors
• large_response_fixture.json: Fixture data for the oversized-payload edge case test

Candidate Data Schema

Column	Type	Description
candidate_id	string	Unique candidate identifier
requisition_id	string	Job requisition ID (e.g., "05958BR")
requisition_template_id	string	Template for similar requisitions
source_name	string	Sourcing channel (LinkedIn, Dice, GitHub, etc.)
applied_at	datetime	Application timestamp
reviewed	bool	Whether candidate was reviewed
sla_met	bool	Whether SLA was met for review
interviewed	bool	Whether candidate was interviewed
offer_extended	bool	Whether offer was extended
offer_accepted	bool	Whether offer was accepted
hired	bool	Whether candidate was hired
hire_date	datetime	Date of hire (if hired)
skills	list	Candidate skills list
department	string	Job department
seniority_level	string	Job seniority level

Task Format

Each task in tasks.json has:

{
  "name": "task_1",
  "description": "Task description and explanation",
  "intent": "The question to answer",
  "difficulty": "easy|medium|hard",
  "expected_output": {
    "response": "Expected answer text",
    "keywords": ["keyword1", "keyword2|alternative"],
    "tool_calls": [{"name": "api_endpoint", "args": {}}]
  }
}

Keywords support OR matching with | separator.

API Endpoints

The benchmark includes 32 API endpoints (13 core + 19 error-prone).

Core Endpoints (13)

Candidate Source APIs (7)

1. candidate_source_sla_per_source - SLA performance by source
2. candidate_source_total_hires_by_source - Hire counts by source
3. candidate_source_candidate_volume_by_source - Candidate volume metrics
4. candidate_source_funnel_conversion_by_source - Funnel conversion rates
5. candidate_source_metadata_and_timeframe - Data timeframe and metadata
6. candidate_source_definitions_and_methodology - Metric definitions
7. candidate_source_source_recommendation_summary - Source recommendations

Skills APIs (6)

8. skills_skill_analysis - Skill statistics and correlations
9. skills_skill_impact_fill_rate - Skill impact on fill rate
10. skills_skill_impact_sla - Skill impact on SLA
11. skills_skill_relevance_justification - Skill relevance explanation
12. skills_successful_posting_criteria - Success criteria thresholds
13. skills_data_sources_used - Data sources and models used

Error-Prone Endpoints (19)

These endpoints intentionally exhibit problematic behaviors to test agent resilience and error handling.

Type Mismatch (3)

14. skills_skill_summary - Returns plain string instead of JSON
15. candidate_source_source_sla_score - Returns numeric float instead of structured response
16. candidate_source_inactive_sources - Returns boolean or list depending on data state

HTTP Errors (4)

17. candidate_source_candidate_pipeline_status - Intermittently returns 404
18. candidate_source_source_sla_check - Returns 500 Internal Server Error
19. candidate_source_funnel_status - Returns 503 Service Unavailable
20. candidate_source_bulk_source_data - Returns 429 Too Many Requests

Schema Violations (4)

21. skills_model_registry - No output schema; returns untyped dict
22. skills_skill_lookup - Returns extra undeclared fields
23. candidate_source_source_metrics_lite - Randomly omits required fields
24. candidate_source_volume_report - Returns wrong field types (strings for numbers)

Edge Cases (5)

25. candidate_source_full_candidate_details - Oversized payload (~1MB)
26. candidate_source_source_directory - Unicode and special characters
27. skills_skill_deep_analysis - Deeply nested JSON (5+ levels)
28. candidate_source_sla_extended - Unexpected extra fields
29. skills_analyze_skill_match - Mismatched schema vs documentation

Undocumented Behaviors (3)

30. candidate_source_requisition_details - Non-standard error format
31. candidate_source_list_all_sources - Undocumented pagination
32. candidate_source_batch_metrics - Undocumented rate limiting headers

Usage

With the Evaluation Space

The easiest way to use this dataset is through the evaluation Space:

ibm-research/bpo-benchmark-eval

Programmatic Access

from huggingface_hub import hf_hub_download
import pandas as pd
import json

repo = "ibm-research/bpo-benchmark"

# Download candidate data
parquet_path = hf_hub_download(repo, "candidate_data.parquet", repo_type="dataset")

# Download all task suites
task_files = [
    "tasks.json",
    "tasks_type_mismatch.json",
    "tasks_http_errors.json",
    "tasks_schema_violations.json",
    "tasks_edge_cases.json",
    "tasks_undocumented.json",
]
task_paths = {
    f: hf_hub_download(repo, f, repo_type="dataset")
    for f in task_files
}

# Load data
df = pd.read_parquet(parquet_path)
with open(task_paths["tasks.json"]) as f:
    core_tasks = json.load(f)

print(f"Loaded {len(df)} candidates")
print(f"Loaded {len(core_tasks[0]['test_cases'])} core tasks")
print(f"Task suites: {list(task_paths.keys())}")

Statistics

• Candidates: 64,000 records
• Requisitions: 1,047 unique
• Sourcing Channels: 7 (LinkedIn, Dice, GitHub, Indeed, Referral, CyberSec Jobs, Company Website)
• Total API Endpoints: 32 (13 core + 19 error-prone)
• Core Evaluation Tasks: 26 (Easy: 20, Medium: 3, Hard: 3)
• Error-Prone Tasks: 19 (Type Mismatch: 3, HTTP Errors: 4, Schema Violations: 4, Edge Cases: 5, Undocumented: 3)
• Total Tasks: 45
• Time Range: Oct 2023 - Mar 2025

License

Apache 2.0

Paper

From Benchmarks to Business Impact: Deploying IBM Generalist Agent in Enterprise Production https://arxiv.org/abs/2510.23856

Citation

@inproceedings{shlomov2025benchmarks,
  title={From Benchmarks to Business Impact: Deploying IBM Generalist Agent in Enterprise Production},
  author={Shlomov, Segev and Oved, Alon and Marreed, Sami and Levy, Ido and Akrabi, Offer and Yaeli, Avi and Str{\k{a}}k, {\L}ukasz and Koumpan, Elizabeth and Goldshtein, Yinon and Shapira, Eilam and others},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  year={2026}
}