metadata
license: cc-by-nc-4.0
dataset_info:
- config_name: oracle
features:
- name: task_id
dtype: string
- name: domain
dtype: string
- name: system_prompt
dtype: string
- name: user_prompt
dtype: string
- name: selected_tools
list: string
- name: restricted_tools
list: 'null'
- name: mcp_endpoint
dtype: string
- name: number_of_runs
dtype: int64
- name: reset_database_between_runs
dtype: bool
- name: gym_servers_config
dtype: string
- name: verifiers
dtype: string
splits:
- name: calendar
num_bytes: 287514
num_examples: 61
- name: csm
num_bytes: 1463009
num_examples: 103
- name: drive
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num_examples: 64
- name: email
num_bytes: 466399
num_examples: 67
- name: hr
num_bytes: 1691979
num_examples: 102
- name: hybrid
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num_examples: 88
- name: itsm
num_bytes: 1422432
num_examples: 103
- name: teams
num_bytes: 1305140
num_examples: 61
download_size: 991194
dataset_size: 8376979
- config_name: plus_10_tools
features:
- name: task_id
dtype: string
- name: domain
dtype: string
- name: system_prompt
dtype: string
- name: user_prompt
dtype: string
- name: selected_tools
list: string
- name: restricted_tools
list: 'null'
- name: mcp_endpoint
dtype: string
- name: number_of_runs
dtype: int64
- name: reset_database_between_runs
dtype: bool
- name: gym_servers_config
dtype: string
- name: verifiers
dtype: string
splits:
- name: calendar
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num_examples: 59
- name: csm
num_bytes: 1465984
num_examples: 102
- name: drive
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num_examples: 64
- name: email
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num_examples: 67
- name: hr
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num_examples: 102
- name: hybrid
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num_examples: 88
- name: itsm
num_bytes: 1447378
num_examples: 103
- name: teams
num_bytes: 1145920
num_examples: 52
download_size: 982687
dataset_size: 8313508
- config_name: plus_15_tools
features:
- name: task_id
dtype: string
- name: domain
dtype: string
- name: system_prompt
dtype: string
- name: user_prompt
dtype: string
- name: selected_tools
list: string
- name: restricted_tools
list: 'null'
- name: mcp_endpoint
dtype: string
- name: number_of_runs
dtype: int64
- name: reset_database_between_runs
dtype: bool
- name: gym_servers_config
dtype: string
- name: verifiers
dtype: string
splits:
- name: calendar
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num_examples: 59
- name: csm
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num_examples: 102
- name: drive
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num_examples: 64
- name: email
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num_examples: 67
- name: hr
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num_examples: 102
- name: hybrid
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- name: itsm
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num_examples: 103
- name: teams
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num_examples: 52
download_size: 989853
dataset_size: 8374612
- config_name: plus_5_tools
features:
- name: task_id
dtype: string
- name: domain
dtype: string
- name: system_prompt
dtype: string
- name: user_prompt
dtype: string
- name: selected_tools
list: string
- name: restricted_tools
list: 'null'
- name: mcp_endpoint
dtype: string
- name: number_of_runs
dtype: int64
- name: reset_database_between_runs
dtype: bool
- name: gym_servers_config
dtype: string
- name: verifiers
dtype: string
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- name: calendar
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num_examples: 59
- name: csm
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num_examples: 102
- name: drive
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- name: email
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num_examples: 67
- name: hr
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num_examples: 102
- name: hybrid
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num_examples: 88
- name: itsm
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num_examples: 103
- name: teams
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num_examples: 52
download_size: 981152
dataset_size: 8242993
configs:
- config_name: oracle
data_files:
- split: calendar
path: oracle/calendar-*
- split: csm
path: oracle/csm-*
- split: drive
path: oracle/drive-*
- split: email
path: oracle/email-*
- split: hr
path: oracle/hr-*
- split: hybrid
path: oracle/hybrid-*
- split: itsm
path: oracle/itsm-*
- split: teams
path: oracle/teams-*
- config_name: plus_10_tools
data_files:
- split: calendar
path: plus_10_tools/calendar-*
- split: csm
path: plus_10_tools/csm-*
- split: drive
path: plus_10_tools/drive-*
- split: email
path: plus_10_tools/email-*
- split: hr
path: plus_10_tools/hr-*
- split: hybrid
path: plus_10_tools/hybrid-*
- split: itsm
path: plus_10_tools/itsm-*
- split: teams
path: plus_10_tools/teams-*
- config_name: plus_15_tools
data_files:
- split: calendar
path: plus_15_tools/calendar-*
- split: csm
path: plus_15_tools/csm-*
- split: drive
path: plus_15_tools/drive-*
- split: email
path: plus_15_tools/email-*
- split: hr
path: plus_15_tools/hr-*
- split: hybrid
path: plus_15_tools/hybrid-*
- split: itsm
path: plus_15_tools/itsm-*
- split: teams
path: plus_15_tools/teams-*
- config_name: plus_5_tools
data_files:
- split: calendar
path: plus_5_tools/calendar-*
- split: csm
path: plus_5_tools/csm-*
- split: drive
path: plus_5_tools/drive-*
- split: email
path: plus_5_tools/email-*
- split: hr
path: plus_5_tools/hr-*
- split: hybrid
path: plus_5_tools/hybrid-*
- split: itsm
path: plus_5_tools/itsm-*
- split: teams
path: plus_5_tools/teams-*
EnterpriseOps-Gym: Environments and Evaluations for Stateful Agentic Planning and Tool Use in Enterprise Settings
EnterpriseOps-Gym is a containerized, resettable enterprise simulation benchmark for evaluating LLM agents on stateful, multi-step planning and tool use across realistic enterprise workflows
About
EnterpriseOps-Gym is a large-scale benchmark for evaluating the agentic planning and tool-use capabilities of LLM agents across enterprise operations. It comprises 1,150 expert-curated tasks spanning 8 enterprise domains, each running against live containerized MCP servers backed by realistic, fully synthetic databases.
Unlike static QA benchmarks, EnterpriseOps-Gym evaluates agents on final environment state using SQL verifiers - meaning agents are rewarded for achieving the correct outcome, not for following a rigid action sequence. Tasks require long-horizon multi-step reasoning, strict policy compliance, and precise tool invocation under complex data dependencies.
Best model performance: 34.1% success rate - leaving significant headroom for future research.
Key Features
- 🛠️ 512 tools across 8 enterprise domains
- 🗄️ 164 database tables with avg 1.7 foreign-key dependencies per table
- 🔢 9.15 avg steps per task (up to 34), with 5.3 avg verification conditions
- 📏 89k avg context length per task
- 🔒 Tasks enforce access control, policy compliance, and referential integrity
- ✅ Evaluation is outcome-based via executable SQL verifiers — not action-sequence matching
- 🐳 Fully containerized sandbox — reproducible and isolated per task run
Evaluation Framework
The evaluation code is available at ServiceNow/EnterpriseOps-Gym.
The framework supports:
- Multiple orchestrators: ReAct, Planner-ReAct, Decomposing Planner
- Multiple LLM providers: Anthropic, OpenAI, Azure OpenAI, Google Gemini, DeepSeek, vLLM, and more
- Parallel execution via Ray for large-scale runs
- Automatic scoring with per-task and per-mode breakdowns
from datasets import load_dataset
ds = load_dataset("ServiceNow-AI/EnterpriseOps-Gym", "oracle", split="teams")
Domain Information
The dataset is organized by domain (split) and mode (configuration subset).
Domains
| Domain | Tasks | Avg Steps | Max Steps | Tools |
|---|---|---|---|---|
| Calendar | 100 | 7.05 | 17 | 37 |
| CSM | 186 | 12.10 | 27 | 89 |
| Drive | 105 | 8.68 | 29 | 55 |
| 104 | 6.25 | 22 | 79 | |
| HR | 184 | 10.54 | 34 | 89 |
| ITSM | 181 | 9.00 | 31 | 93 |
| Teams | 100 | 9.41 | 18 | 70 |
| Hybrid | 155 | 7.79 | 19 | Multi-domain |
| Total | 1,115 | 9.15 | 34 | 512 |
Modes (Tool-Set Configurations)
Each mode controls the set of tools exposed to the agent, simulating realistic tool-retrieval scenarios:
| Mode | Description |
|---|---|
oracle |
Only the exact tools needed for the task |
plus_5_tools |
Oracle tools + 5 randomly sampled distractor tools |
plus_10_tools |
Oracle tools + 10 randomly sampled distractor tools |
plus_15_tools |
Oracle tools + 15 randomly sampled distractor tools |
Field Descriptions
Each row in the dataset corresponds to one task instance and contains the following fields:
| Field | Type | Description |
|---|---|---|
task_id |
string |
Unique identifier for the task |
domain |
string |
Domain name (e.g., teams, csm, hr) |
system_prompt |
string |
Agent role definition and domain-specific policies |
user_prompt |
string |
Natural language task instruction |
verifiers |
string (JSON) |
Array of SQL-based outcome verification scripts that check final environment state |
gym_servers_config |
string (JSON) |
MCP server configuration(s) specifying which containerized gym server(s) to connect to |
selected_tools |
list[string] |
Names of tools available to the agent in this mode |
Example Use Cases
EnterpriseOps-Gym can be used for:
- Benchmarking LLM agents on realistic enterprise workflows across IT, HR, CRM, and collaboration domains
- Evaluating tool-use and planning under long-horizon, multi-step, policy-constrained settings
- Studying tool retrieval robustness by comparing oracle vs. distractor-augmented tool modes
- Developing new orchestration strategies — the framework natively supports ReAct, Planner-ReAct, and Decomposing Planner
- Studying failure modes of state-of-the-art models on high-complexity enterprise tasks (best model: 34.1%)
- Extending the benchmark with new domains, tasks, or verifiers using the released Docker sandbox infrastructure
Citation
@misc{enterpriseopsgym2026,
title = {EnterpriseOps-Gym: Environments and Evaluations for Stateful Agentic Planning and Tool Use in Enterprise Settings},
author = {},
year = {2026}
}