--- 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 num_bytes: 469510 num_examples: 64 - name: email num_bytes: 466399 num_examples: 67 - name: hr num_bytes: 1691979 num_examples: 102 - name: hybrid num_bytes: 1270996 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 num_bytes: 291906 num_examples: 59 - name: csm num_bytes: 1465984 num_examples: 102 - name: drive num_bytes: 481171 num_examples: 64 - name: email num_bytes: 478847 num_examples: 67 - name: hr num_bytes: 1714711 num_examples: 102 - name: hybrid num_bytes: 1287591 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 num_bytes: 296762 num_examples: 59 - name: csm num_bytes: 1473824 num_examples: 102 - name: drive num_bytes: 486100 num_examples: 64 - name: email num_bytes: 484265 num_examples: 67 - name: hr num_bytes: 1726316 num_examples: 102 - name: hybrid num_bytes: 1295972 num_examples: 88 - name: itsm num_bytes: 1460550 num_examples: 103 - name: teams num_bytes: 1150823 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 splits: - name: calendar num_bytes: 286286 num_examples: 59 - name: csm num_bytes: 1450583 num_examples: 102 - name: drive num_bytes: 475636 num_examples: 64 - name: email num_bytes: 473047 num_examples: 67 - name: hr num_bytes: 1703152 num_examples: 102 - name: hybrid num_bytes: 1279121 num_examples: 88 - name: itsm num_bytes: 1434621 num_examples: 103 - name: teams num_bytes: 1140547 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-* ---

Logo 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

EnterpriseOps-Gym Overview
## 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{enterpriseopsgym2026, title = {EnterpriseOps-Gym: Environments and Evaluations for Stateful Agentic Planning and Tool Use in Enterprise Settings}, author = {}, year = {2026} }