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
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num_examples: 103
- 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
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num_examples: 103
- name: teams
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num_examples: 61
download_size: 991194
dataset_size: 8376979
- config_name: plus_10_tools
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- name: task_id
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- 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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num_examples: 88
- name: itsm
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num_examples: 103
- name: teams
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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
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- name: csm
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- name: drive
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- name: email
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- name: hr
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- config_name: plus_5_tools
features:
- name: task_id
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- name: domain
dtype: string
- name: system_prompt
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- name: user_prompt
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- name: selected_tools
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- name: restricted_tools
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- name: verifiers
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- name: csm
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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{malay2026enterpriseopsgymenvironmentsevaluationsstateful,
title={EnterpriseOps-Gym: Environments and Evaluations for Stateful Agentic Planning and Tool Use in Enterprise Settings},
author={Shiva Krishna Reddy Malay and Shravan Nayak and Jishnu Sethumadhavan Nair and Sagar Davasam and Aman Tiwari and Sathwik Tejaswi Madhusudhan and Sridhar Krishna Nemala and Srinivas Sunkara and Sai Rajeswar},
year={2026},
eprint={2603.13594},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2603.13594},
}