README.md

metadata

license: other
language:
  - en
tags:
  - servicenow
  - workflows
  - business-rules
  - agents
  - benchmark
pretty_name: Cascade Bench
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train.jsonl

Cascade Bench

A benchmark of ServiceNow workflow trajectories ("World of Workflows++"). Each row is a single tool action applied to a domain, together with the seed/supporting data, the table schemas, the business rules that can fire, and the resulting cascade of execution logs and audit records.

Summary

• 37 samples, one per workflow domain (e.g. accounts_payable_processing, incident_escalation, change_management).
• One split: train.

Columns

Column	Type	Description
domain	string	Workflow domain / sample id
topology	string	Cascade topology (e.g. linear)
total_brs_fired	int	Business rules that actually fired
expected_br_count	int	Expected number of business rules
audit_count	int	Number of deduped audit records
raw_audit_count	int	Number of raw audit records
tool_name	string	The action invoked
parameters	string (JSON)	{table_name, operation, fields} — fields keys are domain-specific
seed_data	string (JSON)	Initial record state for the target table
supporting_data	string (JSON)	Map of related table name → rows
schema	string (JSON)	Map of table name → column schema
business_rules	list[struct]	name, fires_on_table, filter_condition, trigger_sequence, trigger_type, order, script
ewm_logs	list[struct]	Execution-log entries (u_br_name, u_table_name, u_field_name, u_old_value, u_new_value, …)
audits	list[struct]	Deduped field-level audit (tablename, fieldname, oldvalue, newvalue, documentkey)
raw_audits	list[struct]	Raw field-level audit, same shape as audits

The four JSON-string columns (parameters, seed_data, supporting_data, schema) hold dicts whose keys (table/field names) differ per domain, so they are stored as serialized JSON to keep a stable Arrow schema. Call json.loads() to use them.

Usage

from datasets import load_dataset
import json

ds = load_dataset("ServiceNow-AI/cascade_bench", split="train")

row = ds[0]
print(row["domain"], row["tool_name"])
schema = json.loads(row["schema"])          # variable-key dict
params = json.loads(row["parameters"])
brs = row["business_rules"]                  # native list of structs