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# All rights reserved.
"""
Data models for the PostMortem incident-triage environment.
An agent plays an on-call SRE responding to a live incident. It queries fake
telemetry (logs / metrics / traces), scopes blast radius, hypothesises a root
cause, applies a mitigation, and writes a status-page update. The reward is a
5-stage process-reward ladder in [0, 1].
"""
from typing import Any, Dict, List
from openenv.core.env_server.types import Action, Observation
from pydantic import Field
class PostmortemAction(Action):
"""
Single action envelope. `tool` selects an investigation / response verb,
`args` is a tool-specific dict.
Supported tools:
- "ack" args: {}
- "query_logs" args: {"service": str}
- "query_metrics" args: {"service": str}
- "query_traces" args: {"trace_id": str}
- "scope" args: {"services": list[str]}
- "hypothesize" args: {"root_cause": str}
- "mitigate" args: {"action": str}
- "write_status" args: {"text": str}
"""
tool: str = Field(..., description="Investigation/response verb")
args: Dict[str, Any] = Field(default_factory=dict, description="Tool args")
class PostmortemObservation(Observation):
"""Observation returned after each step."""
task_id: str = Field(default="", description="Current scenario id")
task_description: str = Field(default="", description="Incident brief")
available_services: List[str] = Field(default_factory=list)
available_trace_ids: List[str] = Field(default_factory=list)
tool_result: str = Field(default="", description="Result from the last tool call")
subgoals: Dict[str, bool] = Field(
default_factory=lambda: {
"acked": False,
"scoped": False,
"hypothesized": False,
"mitigated": False,
"written": False,
}
)
reward_so_far: float = Field(default=0.0)
steps_remaining: int = Field(default=0)
last_error: str = Field(default="")
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