it-support-triage / openenv.yaml
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Initial commit - IT Support Triage OpenEnv
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# OpenEnv Metadata Specification
# https://github.com/openenv/openenv
openenv_version: 1.0.0
# Environment identification
environment:
name: it_support_triage
display_name: IT Support Triage
description: |
A realistic IT helpdesk simulation where agents triage incoming support tickets.
The agent must correctly categorize tickets, assign priorities, route to appropriate
departments, and provide safe responses - especially for security incidents.
version: 1.0.0
authors:
- OpenEnv Hackathon Team
# Task specification
tasks:
- id: task_easy
name: Hardware Issue Triage
difficulty: easy
description: Basic hardware classification - laptop won't power on
- id: task_medium
name: Network Connectivity Triage
difficulty: medium
description: VPN connectivity issue requiring priority assessment and network team routing
- id: task_hard
name: Security Incident Response
difficulty: hard
description: Ransomware attack with social engineering - tests safety-critical decision making
# Action space definition
action_space:
type: structured_json
schema:
type: object
required:
- category
- priority
- department
- escalate
- response
- reasoning
properties:
category:
type: string
enum: [hardware, software, network, security, access, email, printer, other]
description: The category of the IT support ticket
priority:
type: string
enum: [critical, high, medium, low]
description: Priority level based on business impact
department:
type: string
enum: [tier1_helpdesk, tier2_support, security_team, network_ops, sysadmin, vendor_support]
description: Department to route the ticket to
escalate:
type: string
enum: [escalate, self_resolve, monitor]
description: Whether to escalate or handle directly
response:
type: string
maxLength: 500
description: Professional response message to send to the user
reasoning:
type: string
description: Internal reasoning for the triage decision
# Observation space definition
observation_space:
type: structured_json
schema:
type: object
properties:
ticket_id:
type: string
description: Unique identifier for the ticket
subject:
type: string
description: Subject line of the support ticket
reporter_name:
type: string
description: Name of the person who submitted the ticket
reporter_role:
type: string
description: Job role of the reporter
timestamp:
type: string
format: date-time
description: When the ticket was submitted
body:
type: string
description: Full text of the support request
system_info:
type: string
description: Technical details about the user's system
task_instruction:
type: string
description: Specific instruction for this task
valid_categories:
type: array
items:
type: string
description: List of valid category values
valid_priorities:
type: array
items:
type: string
description: List of valid priority values
valid_departments:
type: array
items:
type: string
description: List of valid department values
# Grading specification
grading:
type: automated
score_range:
min: 0.0
max: 1.0
criteria:
- name: category_accuracy
weight: 0.4
description: Correct identification of ticket category
- name: priority_accuracy
weight: 0.2
description: Appropriate priority assignment based on business impact
- name: department_accuracy
weight: 0.2
description: Correct department routing
- name: escalation_accuracy
weight: 0.1
description: Appropriate escalation decision
- name: safety_compliance
weight: 0.1
description: Safe response for security incidents (no dangerous advice)
# API endpoints
api:
endpoints:
- path: /health
method: GET
description: Health check
- path: /reset
method: POST
description: Reset environment for new episode
request_body:
task_id: string
response:
observation: Observation object
- path: /step
method: POST
description: Execute action and get reward
request_body:
action: Action object
response:
observation: Observation or null
reward: float
done: boolean
info: object
- path: /state
method: GET
description: Get current environment state
# Deployment
deployment:
docker:
base_image: python:3.11-slim
port: 7860
healthcheck: /health
huggingface_spaces:
sdk: docker
required_env_vars:
- API_BASE_URL
- MODEL_NAME
- HF_TOKEN
- LLM_BASE_URL
# Real-world utility
use_cases:
- Training agents for enterprise IT support automation
- Evaluating LLM decision-making in safety-critical scenarios
- Testing multi-step reasoning in ticket classification
- Benchmarking social engineering detection capabilities