| --- |
| title: Customer Support Triage |
| emoji: 🎧 |
| colorFrom: blue |
| colorTo: indigo |
| sdk: docker |
| app_port: 8000 |
| pinned: false |
| --- |
| |
| # Customer Support Triage Environment |
|
|
| A real-world OpenEnv environment for the OpenEnv Hackathon. It simulates a Customer Support Triage system where an AI agent reads inbound support tickets and decides how to handle each one correctly. |
|
|
| ## Environment Description |
|
|
| The agent reads one ticket at a time from an inbox and must choose the correct triage action. Tickets span categories like password resets, billing issues, churn risk, security vulnerabilities, and vague complaints. |
|
|
| ### Action Space |
|
|
| | Action | Required Fields | When to Use | |
| |---|---|---| |
| | `assign` | `department`, `priority` | Route clear tickets to the correct department | |
| | `ask_user` | `reply_text` | Request more info for vague/ambiguous tickets | |
| | `escalate` | — | Handle critical churn or security tickets immediately | |
|
|
| **Available departments:** `TechSupport`, `Billing`, `Sales`, `Retention` |
| **Available priorities:** `Low`, `Medium`, `High`, `Urgent` |
|
|
| ### Observation Space |
|
|
| Each step returns: |
| - `active_ticket_id`: ID of the current ticket |
| - `ticket_content`: Full text of the ticket |
| - `ticket_metadata`: Dict with ticket `type` |
| - `unresolved_count`: Number of remaining open tickets |
| - `tickets_summary`: Summary list of all tickets and their statuses |
| - `reward`: Reward from the last action (0.0 or 1.0) |
| - `done`: Whether the episode has ended |
|
|
| ### Reward Function |
|
|
| - **+1.0** for each correctly handled ticket (right department, right escalation, or right ask_user) |
| - **+0.0** for incorrect routing |
| - Final score = `sum(rewards) / total_tickets` (clipped to [0.0, 1.0]) |
|
|
| ## Task Difficulties |
|
|
| | Task | Difficulty | Tickets | Description | |
| |---|---|---|---| |
| | `task1` | Easy | 1 | Route a single password reset ticket to TechSupport | |
| | `task2` | Medium | 3 | Route a billing ticket, a password ticket, and one vague ticket requiring `ask_user` | |
| | `task3` | Hard | 5 | Mixed inbox: password reset, churn threat, security bypass, billing overcharge, sales inquiry | |
|
|
| ## Setup & Usage |
|
|
| ### Prerequisites |
|
|
| ```bash |
| pip install openenv-core fastapi uvicorn pydantic openai |
| ``` |
|
|
| ### Run locally |
|
|
| ```bash |
| # 1. Start the environment server |
| uvicorn server.app:app --host 0.0.0.0 --port 8000 |
| |
| # 2. Set API credentials |
| export API_BASE_URL="https://api.openai.com/v1" |
| export MODEL_NAME="gpt-4o-mini" |
| export HF_TOKEN="<your-openai-or-hf-api-key>" |
| |
| # 3. Run the baseline inference script |
| python inference.py |
| ``` |
|
|
| ### Run with Docker |
|
|
| ```bash |
| docker build -t customer-support-env . |
| docker run -p 8000:8000 customer-support-env |
| |
| # Then in another terminal: |
| LOCAL_IMAGE_NAME=customer-support-env python inference.py |
| ``` |
|
|
| ## Baseline Results |
|
|
| Tested with `gpt-4o-mini` via `api.openai.com/v1`: |
|
|
| | Task | Steps | Score | |
| |---|---|---| |
| | task1 | 1 | 0.950 | |
| | task2 | 3 | 0.950 | |
| | task3 | 5 | 0.950 | |
|
|
| The environment is fully deterministic with unambiguous grading — the correct action for every ticket type is uniquely defined. Scores are strictly within (0, 1) as required by the OpenEnv spec. |
|
|
| ## Project Structure |
|
|
| ``` |
| customer_support_env/ |
| ├── inference.py # Baseline inference script (mandatory) |
| ├── client.py # OpenEnv WebSocket client |
| ├── models.py # Pydantic Action & Observation models |
| ├── openenv.yaml # OpenEnv spec config |
| ├── pyproject.toml # Environment packaging spec |
| ├── uv.lock # Dependency lock file |
| ├── validate-submission.sh # Validation script |
| ├── Dockerfile # Container definition |
| ├── server/ |
| │ ├── app.py # FastAPI app wrapping the environment |
| │ └── environment.py # Core environment logic & grader |
| └── README.md |
| ``` |
|
|
