Pass instruction depth config to llm-connect

tests/test_instruction_evaluation.py

@@ -10,10 +10,15 @@ from activity_core import activities
 class FakeLLMClient:
     def __init__(self, response: str) -> None:
         self.response = response
-        self.calls: list[tuple[str, str]] = []
+        self.calls: list[tuple[str, str, dict | None]] = []
 
-    def complete(self, prompt: str, model: str = "") -> str:
-        self.calls.append((prompt, model))
+    def complete(
+        self,
+        prompt: str,
+        model: str = "",
+        config: dict | None = None,
+    ) -> str:
+        self.calls.append((prompt, model, config))
         return self.response
 
 
@@ -56,7 +61,9 @@ async def test_evaluate_instructions_returns_task_specs_with_audit(monkeypatch)
     assert spec["prompt_hash"] is not None
     assert len(spec["prompt_hash"]) == 64
     assert result["reports"] == []
-    assert llm.calls == [("Open tasks: 3", "test-model")]
+    assert llm.calls == [
+        ("Open tasks: 3", "test-model", {"model_name": "test-model"})
+    ]
 
 
 @pytest.mark.asyncio
@@ -94,7 +101,12 @@ async def test_evaluate_instructions_returns_report_payload(monkeypatch) -> None
 @pytest.mark.asyncio
 async def test_evaluate_instructions_without_llm_client_returns_no_tasks(monkeypatch) -> None:
     class RaisingClient:
-        def complete(self, prompt: str, model: str = "") -> str:  # noqa: ARG002
+        def complete(
+            self,
+            prompt: str,
+            model: str = "",
+            config: dict | None = None,
+        ) -> str:  # noqa: ARG002
             raise RuntimeError("not configured")
 
     monkeypatch.setattr(activities, "get_llm_client", lambda: RaisingClient())
@@ -114,3 +126,36 @@ async def test_evaluate_instructions_without_llm_client_returns_no_tasks(monkeyp
     })
 
     assert result == {"task_specs": [], "reports": []}
+
+
+@pytest.mark.asyncio
+async def test_evaluate_instructions_forwards_llm_connect_depth_config(monkeypatch) -> None:
+    llm = FakeLLMClient(json.dumps({"summary": "ok", "recommendations": []}))
+    monkeypatch.setattr(activities, "get_llm_client", lambda: llm)
+
+    await activities.evaluate_instructions({
+        "instructions": [
+            {
+                "id": "daily-triage-report",
+                "trusted_fields": [],
+                "model": "custodian-triage-balanced",
+                "temperature": 0.2,
+                "max_tokens": 1200,
+                "max_depth": 2,
+                "model_params": {"reasoning_effort": "medium"},
+                "prompt": "Run report.",
+                "output_schema": "schemas/daily-triage-report.json",
+                "review_required": False,
+            }
+        ],
+        "event": {},
+        "context": {},
+    })
+
+    assert llm.calls[0][2] == {
+        "model_name": "custodian-triage-balanced",
+        "temperature": 0.2,
+        "max_tokens": 1200,
+        "max_depth": 2,
+        "model_params": {"reasoning_effort": "medium"},
+    }