feat(platform): close AI expense value loop

Add tenant-safe value, telemetry, connector, commercial, and production-readiness foundations.

server/tests/test_agent_asset_release_recall.py

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+from __future__ import annotations
+
+import pytest
+
+from app.services.agent_asset_release_guard import ReleaseEvaluationInput
+from app.services.agent_asset_release_policy import (
+    ReleaseGuardPolicy,
+    evaluate_release,
+)
+from app.services.agent_asset_release_recall import estimate_release_recall
+
+
+def test_recall_uses_random_stratum_to_estimate_full_negative_population() -> None:
+    result = estimate_release_recall(
+        true_positive_count=90,
+        disagreement_false_negative_count=2,
+        random_negative_population_count=800,
+        random_reviewed_count=80,
+        random_false_negative_count=1,
+    )
+
+    assert result.estimated_false_negative_count == 12.0
+    assert result.recall == pytest.approx(90 / 102, abs=1e-6)
+    assert result.recall_lower_bound is not None
+    assert result.recall_lower_bound < result.recall
+    assert result.false_negative_upper_bound is not None
+    assert result.false_negative_upper_bound > result.estimated_false_negative_count
+    assert result.method == "stratified_random_audit_wilson_upper_bound"
+
+
+def test_recall_is_exact_when_no_random_negative_stratum_exists() -> None:
+    result = estimate_release_recall(
+        true_positive_count=18,
+        disagreement_false_negative_count=2,
+        random_negative_population_count=0,
+        random_reviewed_count=0,
+        random_false_negative_count=0,
+    )
+
+    assert result.recall == 0.9
+    assert result.recall_lower_bound == 0.9
+    assert result.estimated_false_negative_count == 2.0
+
+
+def test_recall_remains_unavailable_before_random_audit_evidence_exists() -> None:
+    result = estimate_release_recall(
+        true_positive_count=10,
+        disagreement_false_negative_count=0,
+        random_negative_population_count=25,
+        random_reviewed_count=0,
+        random_false_negative_count=0,
+    )
+
+    assert result.recall is None
+    assert result.recall_lower_bound is None
+    assert result.estimated_false_negative_count is None
+
+
+def test_zero_true_positives_with_confirmed_miss_has_zero_recall() -> None:
+    result = estimate_release_recall(
+        true_positive_count=0,
+        disagreement_false_negative_count=1,
+        random_negative_population_count=0,
+        random_reviewed_count=0,
+        random_false_negative_count=0,
+    )
+
+    assert result.recall == 0.0
+    assert result.recall_lower_bound == 0.0
+
+
+@pytest.mark.parametrize(
+    "kwargs",
+    [
+        {"random_reviewed_count": 3, "random_negative_population_count": 2},
+        {"random_false_negative_count": 2, "random_reviewed_count": 1},
+        {"true_positive_count": -1},
+        {"confidence_level": 0.92},
+    ],
+)
+def test_recall_rejects_inconsistent_evidence(kwargs: dict[str, int | float]) -> None:
+    values: dict[str, int | float] = {
+        "true_positive_count": 1,
+        "disagreement_false_negative_count": 0,
+        "random_negative_population_count": 2,
+        "random_reviewed_count": 1,
+        "random_false_negative_count": 0,
+        "confidence_level": 0.95,
+    }
+    values.update(kwargs)
+
+    with pytest.raises(ValueError):
+        estimate_release_recall(**values)  # type: ignore[arg-type]
+
+
+def test_release_gate_collects_without_blind_negative_ground_truth() -> None:
+    result = evaluate_release(
+        "shadow",
+        ReleaseEvaluationInput(
+            total=20,
+            failure_count=0,
+            precision=1.0,
+            details={
+                "metric_source": "release_runtime_telemetry",
+                "negative_ground_truth_status": "insufficient_random_negative_reviews",
+                "recall_lower_bound": None,
+            },
+        ),
+        ReleaseGuardPolicy(shadow_min_samples=1).to_dict(),
+    )
+
+    assert result["status"] == "collecting"
+    assert result["reasons"] == ["insufficient_random_negative_reviews"]
+
+
+def test_release_gate_uses_recall_confidence_lower_bound_not_point_estimate() -> None:
+    policy = ReleaseGuardPolicy(shadow_min_samples=1, min_recall=0.95).to_dict()
+    result = evaluate_release(
+        "shadow",
+        ReleaseEvaluationInput(
+            total=20,
+            failure_count=0,
+            precision=1.0,
+            details={
+                "metric_source": "release_runtime_telemetry",
+                "negative_ground_truth_status": "available_stratified_random_audit",
+                "recall": 0.98,
+                "recall_lower_bound": 0.9,
+            },
+        ),
+        policy,
+    )
+
+    assert result["status"] == "failed"
+    assert "recall_lower_bound_below_threshold" in result["reasons"]
+
+
+def test_release_gate_passes_when_precision_recall_and_samples_are_sufficient() -> None:
+    result = evaluate_release(
+        "shadow",
+        ReleaseEvaluationInput(
+            total=20,
+            failure_count=0,
+            precision=0.99,
+            details={
+                "metric_source": "release_runtime_telemetry",
+                "negative_ground_truth_status": "available_stratified_random_audit",
+                "recall": 0.98,
+                "recall_lower_bound": 0.96,
+            },
+        ),
+        ReleaseGuardPolicy(shadow_min_samples=20, min_recall=0.95).to_dict(),
+    )
+
+    assert result["status"] == "passed"