| --- |
| 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 |
|
|
| ```python |
| 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 |
| ``` |
|
|
