--- 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{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}, }