Added Readme.md

README.md

@@ -279,3 +279,115 @@ configs:
   - 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="#"><img src="https://img.shields.io/badge/Website-blue?logo=google-chrome&logoColor=white" /></a>
+  <a href="#"><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>
+
+![EnterpriseOps-Gym Overview](assets/teaser.png)
+
+## 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}
+}