fix(orchestrator): Remove clarification interrupt to allow single-pass generation

Also fixes a test assertion in the reflector test to align with LangGraph state updates.

backend/src/ea_chatbot/graph/nodes/clarification.py

@@ -36,8 +36,7 @@ Please ask the user for the necessary details."""
         response = llm.invoke(messages)
         logger.info("[bold green]Clarification generated.[/bold green]")
         return {
-            "messages": [response],
+            "messages": [response]
-            "next_action": "end" # To indicate we are done for now
         }
     except Exception as e:
         logger.error(f"Failed to generate clarification: {str(e)}")

backend/src/ea_chatbot/graph/workflow.py

@@ -106,10 +106,8 @@ def create_workflow(
     workflow.add_edge("summarize_conversation", END)
     workflow.add_edge("clarification", END)
     
-    # Compile the graph with human-in-the-loop interrupt
+    # Compile the graph
-    app = workflow.compile(
+    app = workflow.compile()
-        interrupt_before=["clarification"]
-    )
     
     return app
 

backend/tests/test_review_fix_clarification.py

@@ -0,0 +1,55 @@
+import pytest
+from unittest.mock import MagicMock, patch
+from ea_chatbot.graph.workflow import create_workflow
+from ea_chatbot.graph.state import AgentState
+from langchain_core.messages import HumanMessage, AIMessage
+
+def test_clarification_flow_immediate_execution():
+    """Verify that an ambiguous query immediately executes the clarification node without interruption."""
+    
+    mock_analyzer = MagicMock()
+    mock_clarification = MagicMock()
+    
+    # 1. Analyzer returns 'clarify'
+    mock_analyzer.return_value = {"next_action": "clarify"}
+    
+    # 2. Clarification node returns a question
+    mock_clarification.return_value = {"messages": [AIMessage(content="What year?")]}
+    
+    # Create workflow without other nodes since they won't be reached
+    # We still need to provide mock planners etc. to create_workflow
+    app = create_workflow(
+        query_analyzer=mock_analyzer,
+        clarification=mock_clarification,
+        planner=MagicMock(),
+        delegate=MagicMock(),
+        data_analyst_worker=MagicMock(),
+        researcher_worker=MagicMock(),
+        reflector=MagicMock(),
+        synthesizer=MagicMock(),
+        summarize_conversation=MagicMock()
+    )
+    
+    initial_state = AgentState(
+        messages=[HumanMessage(content="Who won?")],
+        question="Who won?",
+        analysis={},
+        next_action="",
+        iterations=0,
+        checklist=[],
+        current_step=0,
+        vfs={},
+        plots=[],
+        dfs={}
+    )
+    
+    # Run the graph
+    final_state = app.invoke(initial_state)
+    
+    # Assertions
+    assert mock_analyzer.called
+    assert mock_clarification.called
+    
+    # Verify the state contains the clarification message
+    assert len(final_state["messages"]) > 0
+    assert "What year?" in [m.content for m in final_state["messages"]]

backend/tests/test_review_fix_reflector.py

@@ -27,7 +27,9 @@ def test_reflector_does_not_advance_on_failure():
         
         result = reflector_node(state)
         
-        # Should NOT increment
+        # Should NOT increment (therefore not in the updates dict)
-        assert result["current_step"] == 0
+        assert "current_step" not in result
+        # Should increment iterations
+        assert result["iterations"] == 1
         # Should probably route to planner or retry
         assert result["next_action"] == "delegate" # Or 'planner' if we want re-planning