PROJECT.md

Support Queue OpenEnv

A real-world OpenEnv benchmark for SaaS support triage.

Agents must read incoming support tickets, assign the right priority, route the case to the correct internal queue, choose the next action, and draft a safe first reply. The benchmark is designed to feel like an actual support operations workflow rather than a toy task.

Why This Environment

Real support teams repeatedly solve the same high-value triage problems:

• decide how urgent a ticket is
• route it to the right team
• avoid unsafe or misleading replies
• handle ambiguous requests without over-escalating

This makes support triage a strong RL and agent-evaluation environment because success is measurable, partial credit is meaningful, and mistakes are easy to interpret.

What The Agent Does

For each ticket, the agent must produce a SupportQueueAction with:

• priority: P1 | P2 | P3 | P4
• queue: billing | security | technical | success | trust_safety
• disposition: respond | request_info | escalate | close
• summary: short internal triage note
• response: first customer-facing reply
• confidence: float in [0.0, 1.0]

Observation Space

Each reset() and step() returns a typed SupportQueueObservation containing:

Field	Meaning
task_id, task_title, difficulty	Active benchmark task metadata
instructions	Task-specific operating guidance
current_index, total_tickets	Episode progress
ticket	Current customer ticket payload
allowed_priorities, allowed_queues, allowed_dispositions	Valid discrete actions
scoring_weights	Reward decomposition
last_feedback	Previous grader output
reward, cumulative_reward, done	Episode feedback
info	Extra metadata such as episode_id

The ticket payload includes:

• ticket_id
• subject
• body
• customer_tier
• product_area
• sla_hours
• recent_events

State Space

state() returns a typed SupportQueueState with:

• active task card
• current cursor
• cumulative and average reward
• processed ticket ids
• full action history
• full per-ticket grading history

Tasks

The benchmark includes three deterministic tasks with increasing difficulty.

Task ID	Difficulty	Tickets	Description
easy_inbox_cleanup	Easy	2	Straightforward access and billing tickets
medium_sla_defense	Medium	3	Mix of phishing escalation, webhook failure, and billing ambiguity
hard_exec_escalations	Hard	4	Executive-pressure tickets spanning production, security, commercial, and retention workflows

Reward Design

Each processed ticket gets a reward in [0.0, 1.0].

Reward components:

Component	Weight
Priority accuracy	0.30
Queue accuracy	0.25
Disposition accuracy	0.20
Summary keyword coverage	0.15
Response keyword coverage	0.10
Unsafe reply penalty	-0.10

This gives useful partial progress signals. An agent can still earn reward for a good route or good reply even if one part of the triage decision is wrong.

API Surface

The environment server exposes:

• POST /reset
• POST /step
• GET /state
• GET /tasks
• GET /health
• GET /

Example reset payload:

{
  "task_id": "easy_inbox_cleanup"
}

Project Structure

support_queue_env/
  client.py
  grading.py
  models.py
  tasks.py
  server/
    app.py
    openenv_compat.py
    support_queue_environment.py
Dockerfile
openenv.yaml
inference.py

Running Locally

Python

pip install -r requirements.txt
uvicorn support_queue_env.server.app:app --host 0.0.0.0 --port 8000

Docker

docker build -t support-queue-openenv .
docker run --rm -p 8000:8000 support-queue-openenv

Baseline Inference

The required inference script is inference.py.

It:

• uses the OpenAI Python client
• reads API_BASE_URL, MODEL_NAME, HF_TOKEN, and optional LOCAL_IMAGE_NAME
• emits structured [START], [STEP], and [END] logs
• writes inference_results.json

Set environment variables:

API_BASE_URL=https://api.openai.com/v1
MODEL_NAME=gpt-4o-mini
HF_TOKEN=your_token
LOCAL_IMAGE_NAME=

Then run:

python inference.py

Baseline Scores

Expected deterministic baseline scores from the bundled heuristic policy:

Task	Score
easy_inbox_cleanup	1.00
medium_sla_defense	0.98
hard_exec_escalations	0.97
Average	0.98

Hugging Face Space

This repository is configured for a Docker Space.

• front matter in README.md sets sdk: docker
• app serves on port 8000
• GET /health and POST /reset support deployment checks

OpenEnv Files

Core submission files:

• openenv.yaml
• inference.py
• Dockerfile
• support_queue_env/models.py
• support_queue_env/server/support_queue_environment.py

Submission Checklist

• typed action, observation, and state models included
• reset(), step(), and state() implemented
• three graded tasks included
• reward bounded to [0.0, 1.0]
• Dockerfile included
• Hugging Face Docker Space compatible
• root inference.py included