environment:
  name: customer-support-agent
  version: "2.0.0"
  description: >
    OpenEnv-compliant autonomous customer support environment.
    Each episode is a 3-phase Markov Decision Process: Triage -> Route -> Resolve.
    An AI agent classifies, assigns, and resolves realistic BPO/customer-support
    tickets across e-commerce and SaaS workflows.
    Deterministic per-phase reward shaping; observation state evolves each step.
  tags:
    - openenv
    - customer-support
    - reinforcement-learning
    - multi-step
    - bpo

tasks:
  - name: easy
    description: >
      3-phase MDP with straightforward tickets. Phase 1: classify issue type.
      Phase 2: route to correct team. Phase 3: respond or refund.
    difficulty: easy
    max_steps: 5
    num_scenarios: 10
    grader: openenv.graders.grader.GraderEngine

  - name: medium
    description: >
      3-phase MDP with moderate complexity. Phase 1: classify. Phase 2: route.
      Phase 3: respond, refund, or escalate safety/product defect issues.
    difficulty: medium
    max_steps: 5
    num_scenarios: 10
    grader: openenv.graders.grader.GraderEngine

  - name: hard
    description: >
      3-phase MDP with ambiguous, high-stakes tickets. Requires distinguishing
      escalate vs refund vs respond with asymmetric business penalties.
    difficulty: hard
    max_steps: 5
    num_scenarios: 10
    grader: openenv.graders.grader.GraderEngine

observation_space:
  type: object
  fields:
    ticket_id:
      type: string
      description: "Unique ticket identifier (e.g. TKT-E001)"
    issue_type:
      type: string
      description: "Category of the customer issue. 'unknown' in Phase 1; revealed after classify."
      enum: [unknown, shipping, billing, technical, returns, safety, cancellation, complaint, orders, sales]
    sentiment:
      type: string
      description: "Detected customer sentiment"
      enum: [positive, neutral, negative, angry]
    priority:
      type: string
      description: "Ticket urgency level"
      enum: [low, medium, high, critical]
    message:
      type: string
      description: "The customer's original message text"
    history:
      type: array
      items:
        type: string
      description: "Phase transition messages and prior agent actions in this episode"

action_space:
  type: object
  fields:
    action_type:
      type: string
      description: "Primary action to execute"
      enum: [classify, assign, respond, refund, escalate]
    team:
      type: string
      nullable: true
      description: "Target team (used with assign/escalate actions)"
      enum: [logistics_team, tech_support_team, safety_team, finance_team, orders_team, management_team]
    response:
      type: string
      nullable: true
      description: >
        In Phase 1: the identified issue_type (e.g. 'billing').
        In Phase 3: customer-facing response message.

reward:
  range: [0.002, 0.998]
  phases:
    phase_1_triage:
      classify_with_correct_issue_type: 0.998
      classify_only: 0.55
      wrong_action: 0.002
    phase_2_route:
      correct_team: 0.998
      wrong_team: 0.400
      wrong_action: 0.002
    phase_3_resolve:
      description: "Proportional quality score: action_type (0.3) + team (0.3) + keyword coverage (0.4)"
      range: [0.002, 0.998]
  penalties:
    unnecessary_refund: -0.50
    unnecessary_escalation: -0.30
    wrong_team: -0.15
    excessive_steps: -0.10 per step beyond 3
  episode_score: "mean(phase_1_reward, phase_2_reward, phase_3_reward) clamped to [0.002, 0.998]"