feat(governance): surface adr100 slo states

apps/api/tests/test_adr100_slo_status_service.py

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+from __future__ import annotations
+
+from typing import Any
+
+import pytest
+
+from src.services.adr100_slo_status_service import Adr100SloStatusService
+
+
+class _FakePrometheusResponse:
+    def __init__(self, payload: dict[str, Any]) -> None:
+        self._payload = payload
+
+    def json(self) -> dict[str, Any]:
+        return self._payload
+
+
+class _FakePrometheusClient:
+    def __init__(self, values: dict[str, str]) -> None:
+        self.values = values
+
+    async def __aenter__(self):
+        return self
+
+    async def __aexit__(self, exc_type, exc, tb):
+        return False
+
+    async def get(self, *args, **kwargs):  # noqa: ANN002, ANN003
+        query = str(kwargs.get("params", {}).get("query", ""))
+        value = self.values.get(query)
+        if value is None:
+            return _FakePrometheusResponse({
+                "status": "success",
+                "data": {"result": []},
+            })
+        return _FakePrometheusResponse({
+            "status": "success",
+            "data": {"result": [{"value": [1778756604, value]}]},
+        })
+
+
+@pytest.mark.asyncio
+async def test_fetch_report_marks_ratio_slos_low_volume(monkeypatch):
+    values = {
+        "sum(rate(automation_operation_log_total[5m]))": "0",
+        'sum(rate(automation_operation_log_total{outcome="auto_executed"}[5m]))': "0",
+        "sum(rate(approval_records_high_confidence_total[1h]))": "0",
+        "max(knowledge_entries_created_24h) or max(sli:km_growth_rate:24h)": "24",
+    }
+
+    monkeypatch.setattr(
+        "httpx.AsyncClient",
+        lambda *args, **kwargs: _FakePrometheusClient(values),
+    )
+
+    report = await Adr100SloStatusService().fetch_report()
+
+    by_name = {metric["name"]: metric for metric in report["metrics"]}
+    assert by_name["decision_accuracy"]["status"] == "skipped_low_volume"
+    assert by_name["decision_accuracy"]["evaluable"] is False
+    assert by_name["confidence_calibration"]["status"] == "skipped_low_volume"
+    assert by_name["km_growth_rate"]["status"] == "ok"
+    assert by_name["km_growth_rate"]["value"] == 24
+    assert report["overall_status"] == "partial"
+    assert report["overall_compliance"] == 1.0
+
+
+@pytest.mark.asyncio
+async def test_fetch_report_classifies_hard_red_line_violation(monkeypatch):
+    values = {
+        "sum(rate(automation_operation_log_total[5m]))": "0.02",
+        "sli:autonomy_rate:5m": "0.5",
+        'sum(rate(automation_operation_log_total{outcome="auto_executed"}[5m]))': "0",
+        "sum(rate(approval_records_high_confidence_total[1h]))": "0",
+        "max(knowledge_entries_created_24h) or max(sli:km_growth_rate:24h)": "3",
+    }
+
+    monkeypatch.setattr(
+        "httpx.AsyncClient",
+        lambda *args, **kwargs: _FakePrometheusClient(values),
+    )
+
+    report = await Adr100SloStatusService().fetch_report()
+
+    by_name = {metric["name"]: metric for metric in report["metrics"]}
+    assert by_name["autonomy_rate"]["status"] == "violated"
+    assert by_name["autonomy_rate"]["sample_count"] == 6
+    assert by_name["km_growth_rate"]["status"] == "violated"
+    assert report["overall_status"] == "violated"