README.md

metadata

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

pip install openenv-core fastapi uvicorn pydantic openai

Run locally

# 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

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