Wire OptimizationLoop to project metrics and add metrics optimize.

Add from_metrics_store factory, OptimizerStore persistence, metrics optimize
CLI, consolidate duplicate optimization agent, and add integration tests.

tests/test_optimization_metrics.py

@@ -0,0 +1,133 @@
+"""Tests for OptimizationLoop integration with MetricsStore."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import pytest
+from click.testing import CliRunner
+
+from kaizen_agentic.cli import cli
+from kaizen_agentic.metrics import MetricsStore, OptimizerStore
+from kaizen_agentic.optimization import (
+    MIN_SAMPLES_FOR_RECOMMENDATIONS,
+    OptimizationLoop,
+)
+
+
+def _seed_executions(
+    store: MetricsStore,
+    count: int,
+    *,
+    success: bool = True,
+    execution_time_s: float = 5.0,
+    quality_score: float = 0.9,
+) -> None:
+    for i in range(count):
+        store.append(
+            {
+                "success": success,
+                "execution_time_s": execution_time_s + i,
+                "quality_score": quality_score,
+            },
+            idempotency_key=f"run-{i}",
+        )
+
+
+@pytest.fixture
+def project_dir(tmp_path: Path) -> Path:
+    root = tmp_path / "demo-project"
+    root.mkdir()
+    return root
+
+
+class TestOptimizationFromMetricsStore:
+    def test_from_metrics_store_loads_execution_records(self, project_dir: Path):
+        store = MetricsStore(project_dir, "tdd-workflow")
+        _seed_executions(store, 3)
+
+        loop = OptimizationLoop.from_metrics_store(store)
+
+        assert len(loop.metrics_history) == 3
+        assert loop.metrics_history[0].success_rate == 1.0
+
+    def test_insufficient_data_recommendations(self, project_dir: Path):
+        store = MetricsStore(project_dir, "tdd-workflow")
+        loop = OptimizationLoop.from_metrics_store(store)
+
+        recommendations = loop.generate_improvement_recommendations()
+
+        assert recommendations[0]["type"] == "info"
+        assert "Insufficient data" in recommendations[0]["message"]
+
+    def test_sufficient_data_produces_performance_recommendations(
+        self, project_dir: Path
+    ):
+        store = MetricsStore(project_dir, "tdd-workflow")
+        _seed_executions(
+            store,
+            MIN_SAMPLES_FOR_RECOMMENDATIONS,
+            success=False,
+            execution_time_s=60.0,
+            quality_score=0.4,
+        )
+
+        loop = OptimizationLoop.from_metrics_store(store)
+        recommendations = loop.generate_improvement_recommendations()
+        types = {item["type"] for item in recommendations}
+
+        assert "info" not in types
+        assert "reliability" in types or "quality" in types or "performance" in types
+
+    def test_get_optimization_report_json_is_serializable(self, project_dir: Path):
+        import json
+
+        store = MetricsStore(project_dir, "coach")
+        _seed_executions(store, 4)
+
+        report = OptimizationLoop.from_metrics_store(store).get_optimization_report_json()
+        json.dumps(report)
+
+
+class TestMetricsOptimizeCli:
+    def test_optimize_insufficient_samples_writes_analysis_only(
+        self, project_dir: Path
+    ):
+        store = MetricsStore(project_dir, "tdd-workflow")
+        _seed_executions(store, 2)
+
+        runner = CliRunner()
+        result = runner.invoke(
+            cli,
+            ["metrics", "optimize", "tdd-workflow", "--target", str(project_dir)],
+        )
+
+        assert result.exit_code == 0
+        assert "need 10" in result.output
+        optimizer = OptimizerStore(project_dir)
+        assert optimizer.analysis_path.exists()
+        assert not optimizer.recommendations_path.exists()
+
+    def test_optimize_sufficient_samples_writes_recommendations(
+        self, project_dir: Path
+    ):
+        store = MetricsStore(project_dir, "tdd-workflow")
+        _seed_executions(
+            store,
+            MIN_SAMPLES_FOR_RECOMMENDATIONS,
+            success=False,
+            execution_time_s=60.0,
+            quality_score=0.4,
+        )
+
+        runner = CliRunner()
+        result = runner.invoke(
+            cli,
+            ["metrics", "optimize", "tdd-workflow", "--target", str(project_dir)],
+        )
+
+        assert result.exit_code == 0
+        optimizer = OptimizerStore(project_dir)
+        assert optimizer.analysis_path.exists()
+        assert optimizer.recommendations_path.exists()
+        assert '"type": "reliability"' in result.output or '"type": "quality"' in result.output