README.md

---
license: cc-by-nc-4.0
dataset_info:
- config_name: oracle
  features:
  - name: task_id
    dtype: string
  - 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: mcp_endpoint
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  - name: number_of_runs
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  - name: reset_database_between_runs
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  - name: gym_servers_config
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  - name: verifiers
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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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  - name: hybrid
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  - name: itsm
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  - name: teams
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    num_examples: 61
  download_size: 991194
  dataset_size: 8376979
- config_name: plus_10_tools
  features:
  - name: task_id
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  - name: domain
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  - 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: mcp_endpoint
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  - name: number_of_runs
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  - name: reset_database_between_runs
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  - name: gym_servers_config
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  - name: verifiers
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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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  - 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: 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
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  - name: reset_database_between_runs
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  - name: gym_servers_config
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  - name: verifiers
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  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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  - 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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  - name: teams
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  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
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  - name: reset_database_between_runs
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  - name: gym_servers_config
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  - name: verifiers
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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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  - name: hybrid
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  - name: itsm
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  - name: teams
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  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-*
---
<div align="center">

<h1><img src="assets/csmgym.png" alt="Logo" width="48" style="vertical-align:middle; margin-right:8px;" /> EnterpriseOps-Gym: Environments and Evaluations for Stateful Agentic Planning and Tool Use in Enterprise Settings</h1>

<p>
  <a href="https://enterpriseops-gym.github.io/"><img src="https://img.shields.io/badge/Website-blue?logo=google-chrome&logoColor=white" /></a>
  <a href="https://arxiv.org/abs/2603.13594"><img src="https://img.shields.io/badge/Paper-red?logo=arxiv&logoColor=white" /></a>
  <a href="https://github.com/ServiceNow/EnterpriseOps-Gym"><img src="https://img.shields.io/badge/GitHub-black?logo=github" /></a>
</p>

<p><i>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</i></p>

</div>

<div align="center"><img src="assets/teaser.png" alt="EnterpriseOps-Gym Overview" width="80%" /></div>

## 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](https://github.com/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](https://www.ray.io/) for large-scale runs
- **Automatic scoring** with per-task and per-mode breakdowns

```python
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 |
| Email | 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

```bibtex
@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}, 
}