# openenv.yaml — OpenEnv manifest for the Customer Support Environment.
# Describes the environment, its models, available tasks, and deployment metadata.

name: customer_support_env
version: "0.2.0"
description: >
  A realistic customer support simulation environment where an AI agent must
  classify tickets, craft empathetic responses, and manage multi-turn
  conversations to resolve customer issues across 5 real-world categories
  and 15 distinct scenarios.

# Pydantic model names (defined in models.py)
action: SupportAction
observation: SupportObservation
state: SupportState

# Available task tiers
tasks:
  - name: easy
    description: >
      Ticket Classification: given a single customer message, the agent must
      output the correct issue category (refund, technical, shipping, billing,
      or account). Scored 0.0, 0.5, or 1.0.
    difficulty: easy
    max_steps: 1

  - name: medium
    description: >
      Single-Turn Response: the agent must write an empathetic, complete reply
      that resolves the customer's issue in one message. Scored 0.0–1.0 based
      on keyword coverage, empathy, response length, and escalation avoidance.
    difficulty: medium
    max_steps: 1

  - name: hard
    description: >
      Multi-Turn Conversation: the agent handles a full 3-turn support
      dialogue — ask a clarifying question, provide a resolution, and close
      the ticket politely. Cumulative score across turns, 0.0–1.0.
    difficulty: hard
    max_steps: 10

# API endpoints
endpoints:
  health: GET /
  reset: POST /reset
  step: POST /step
  state: GET /state
  tasks: GET /tasks
  grader: POST /grader
  baseline: POST /baseline

# Baseline model
baseline_model: llama-3.1-8b-instant
baseline_api: groq

# Placeholder baseline scores (run `python run_baseline.py` to update)
baseline_scores:
  easy: 0.90
  medium: 0.55
  hard: 0.40

tags:
  - customer-support
  - nlp
  - multi-turn
  - classification
  - reward-shaping
  - openenv

author: "sanathkumarps"
hf_space: "sanathkumarps/customer_support_env"