spec_version: 1
name: adaptive-world-env
version: "2.0.0"
type: environment
runtime: docker
app: server/app.py
port: 7860
description: >
  An OpenEnv environment where agents complete multi-step professional tasks
  while the world mutates mid-episode without announcement. API schemas drift,
  fields rename, endpoints version, policies flip, and status meanings change
  silently. The environment tracks belief accuracy separately from task
  completion — revealing whether agents succeed because they understand the
  world or despite misunderstanding it. Difficulty escalates automatically
  based on agent performance. Agent world models persist across episodes.

tasks:
  - id: easy
    name: Syntactic Drift (Field Rename / Endpoint Version / Auth)
    difficulty: easy
    max_steps: 8
    reward_range: [0.0, 1.0]
  - id: medium
    name: Multi-Drift (Policy + Schema Change simultaneously)
    difficulty: medium
    max_steps: 10
    reward_range: [0.0, 1.0]
  - id: hard
    name: Silent Semantic Drift (200 OK, Wrong Meaning)
    difficulty: hard
    max_steps: 12
    reward_range: [0.0, 1.0]
  - id: expert
    name: Adversarial Stability + Cross-Service Drift
    difficulty: expert
    max_steps: 15
    reward_range: [0.0, 1.0]

action_space:
  type: structured
  fields:
    - action_type: string  # call_api | probe_schema | query_history | declare_belief | submit_result
    - method: string
    - url: string
    - headers: dict
    - body: dict
    - query_params: dict
    - belief_state: dict   # agent's declared world model
    - history_steps: int   # used by query_history

observation_space:
  type: structured
  fields:
    - task_description: string
    - domain: string
    - prior_world_model: dict    # agent's beliefs from previous episodes (cross-episode persistence)
    - current_step: int
    - max_steps: int
    - last_status_code: int
    - last_response_body: string
    - last_response_headers: dict
    - step_feedback: string
    - episode_history: list      # returned by query_history action
    - task_reward: float
    - belief_accuracy: float
    - difficulty_level: int      # 0-3, set by adaptive controller
    - reward: float
    - done: bool