fix(agent-team): clarifier turn headroom (max_turns=4) so it can finish its JSON

The clarifier (ClaudeClarifier._turn) called claude_invoke with no max_turns,
inheriting the single-shot default (1). When the model's one turn did not
terminate in a final result the SDK raised 'Reached maximum number of turns (1)'
and, with no salvageable text, the call failed and crashed the clarify node —
leaving the task wedged at clarify with NO question posted to Slack (the human
never sees a clarifier prompt). Observed live on the R720.

Same single-shot flake the planner hit and fixed in PR #58 (_PLANNER_MAX_TURNS=4);
the clarifier never got the headroom. Give it the same: pass max_turns=4 (tools
stay off — still a fast reasoning->JSON completion).

- clarifier_llm.py: _turn passes max_turns=_CLARIFIER_MAX_TURNS (=4).
- tests: clarifier passes max_turns headroom to the invoke seam.

Full suite 1505 passed; ruff clean.

agent-team/agent_team/nodes/clarifier_llm.py

@@ -61,6 +61,16 @@ __all__ = [
 # clarifier loop's injected callables, etc.).
 ClaudeInvoke = Callable[..., ClaudeResult]
 
+# The clarifier is a single-shot reasoning→JSON completion (confidence +
+# question-set), but the invoker's single-shot default (max_turns=1) is flaky:
+# when the model's one turn does not terminate in a final result it raises
+# "Reached maximum number of turns (1)", and with no salvageable text the call
+# fails and crashes the clarify node (leaving the task wedged at clarify with no
+# question posted). The planner hit the same flake and was given headroom in PR
+# #58; the clarifier needs the same. A few turns let the model FINISH its JSON;
+# tools stay OFF so it remains a fast, deterministic completion.
+_CLARIFIER_MAX_TURNS = 4
+
 # Used when the model is below the confidence bar but supplied no usable
 # question-set. The loop must always have something to ask rather than spin or
 # falsely advance, so we substitute a generic clarifier prompt.
@@ -202,7 +212,12 @@ class ClaudeClarifier:
             return self._cache
 
         prompt = self._build_prompt(qa_history, state)
-        result = self._invoke(prompt, model=self._model, config=self._config)
+        result = self._invoke(
+            prompt,
+            model=self._model,
+            config=self._config,
+            max_turns=_CLARIFIER_MAX_TURNS,
+        )
         parsed = self._parse(getattr(result, "text", ""))
 
         self._cache_key = key

agent-team/tests/test_clarifier_llm.py

@@ -89,6 +89,18 @@ def test_high_confidence_parsed() -> None:
     assert clar.assess_confidence([], _state()) == 0.99
 
 
+def test_clarifier_passes_max_turns_headroom_to_invoke_seam() -> None:
+    # The single-shot Claude default (1 turn) is flaky: it crashes the clarify
+    # node with "Reached maximum number of turns (1)" and leaves the task wedged
+    # with no question posted. The clarifier asks for headroom so the model can
+    # FINISH its JSON (mirrors the planner fix, PR #58).
+    fake = _FakeInvoke(_json(0.99, []))
+    clar = ClaudeClarifier(invoke=fake)
+    clar.assess_confidence([], _state())
+    assert fake.calls[0]["kw"].get("max_turns") == 4
+    assert fake.calls[0]["kw"]["max_turns"] > 1
+
+
 def test_single_call_per_turn_memoized() -> None:
     fake = _FakeInvoke(_json(0.99, []))
     clar = ClaudeClarifier(invoke=fake)