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Data models for the Ad Fraud Investigation Environment.
Defines the Action, Observation, and State types used by the OpenEnv interface.
The agent interacts with a queue of ads, investigating them and rendering verdicts.
"""
from __future__ import annotations
from typing import Dict, List, Literal, Optional
from openenv.core.env_server.types import Action, Observation, State
from pydantic import Field
class AdReviewAction(Action):
"""
Action space for the ad fraud investigation agent.
Three action types:
- investigate: Spend budget to reveal information about an ad
- verdict: Approve, reject, or escalate an ad
- link_accounts: Flag two ads as part of the same fraud network
"""
action_type: Literal["investigate", "verdict", "link_accounts"]
ad_id: str = Field(..., description="Target ad identifier (e.g. 'ad_001')")
investigation_target: Optional[
Literal[
"advertiser_history",
"landing_page",
"payment_method",
"targeting_overlap",
"creative_similarity",
"campaign_structure",
]
] = Field(None, description="What to investigate (required for action_type='investigate')")
verdict: Optional[Literal["approve", "reject", "escalate"]] = Field(
None, description="Verdict decision (required for action_type='verdict')"
)
confidence: Optional[float] = Field(
None, ge=0.0, le=1.0, description="Agent's confidence in verdict (0.0-1.0)"
)
linked_ad_id: Optional[str] = Field(
None, description="Other ad in suspected fraud ring (required for action_type='link_accounts')"
)
link_reason: Optional[str] = Field(
None, description="Why the agent believes these ads are connected"
)
class AdReviewObservation(Observation):
"""
Observation returned after each step.
Text-heavy by design so LLM agents can reason about the content naturally.
Structured data is in queue_status for programmatic access.
"""
queue_summary: str = Field(
default="", description="Natural language overview of the ad queue"
)
current_ad_info: str = Field(
default="", description="Details of the ad currently in focus"
)
investigation_findings: str = Field(
default="", description="Accumulated investigation results"
)
verdict_history_summary: str = Field(
default="", description="Summary of verdicts rendered so far"
)
feedback: str = Field(
default="", description="Natural language feedback on the last action taken"
)
available_ads: List[str] = Field(
default_factory=list, description="Ad IDs still pending review"
)
queue_status: Dict = Field(
default_factory=dict,
description="Structured status: total_ads, reviewed, pending, budget, step",
)
class AdFraudState(State):
"""
Internal environment state exposed via the state() property.
Inherits episode_id and step_count from State.
Uses extra='allow' so custom fields are permitted.
"""
task_id: str = ""
total_ads: int = 0
reviewed_count: int = 0
remaining_budget: int = 0
verdicts: Dict = Field(default_factory=dict)
grader_score: Optional[float] = None
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