feat(mma): Implement ConductorEngine and run_worker_lifecycle

tests/test_conductor_engine.py

@@ -0,0 +1,88 @@
+import pytest
+from unittest.mock import MagicMock, patch
+from models import Ticket, Track, WorkerContext
+
+# These tests define the expected interface for multi_agent_conductor.py
+# which will be implemented in the next phase of TDD.
+
+def test_conductor_engine_initialization():
+    """
+    Test that ConductorEngine can be initialized with a Track.
+    """
+    track = Track(id="test_track", description="Test Track")
+    from multi_agent_conductor import ConductorEngine
+    engine = ConductorEngine(track=track)
+    assert engine.track == track
+
+def test_conductor_engine_run_linear_executes_tickets_in_order():
+    """
+    Test that run_linear iterates through executable tickets and calls the worker lifecycle.
+    """
+    ticket1 = Ticket(id="T1", description="Task 1", status="todo", assigned_to="worker1")
+    ticket2 = Ticket(id="T2", description="Task 2", status="todo", assigned_to="worker2", depends_on=["T1"])
+    track = Track(id="track1", description="Track 1", tickets=[ticket1, ticket2])
+    
+    from multi_agent_conductor import ConductorEngine
+    engine = ConductorEngine(track=track)
+    
+    # We mock run_worker_lifecycle as it is expected to be in the same module
+    with patch("multi_agent_conductor.run_worker_lifecycle") as mock_lifecycle:
+        # Mocking lifecycle to mark ticket as complete so dependencies can be resolved
+        def side_effect(ticket, context):
+            ticket.mark_complete()
+            return "Success"
+        mock_lifecycle.side_effect = side_effect
+        
+        engine.run_linear()
+        
+        # Track.get_executable_tickets() should be called repeatedly until all are done
+        # T1 should run first, then T2.
+        assert mock_lifecycle.call_count == 2
+        assert ticket1.status == "completed"
+        assert ticket2.status == "completed"
+        
+        # Verify sequence: T1 before T2
+        calls = mock_lifecycle.call_args_list
+        assert calls[0][0][0].id == "T1"
+        assert calls[1][0][0].id == "T2"
+
+def test_run_worker_lifecycle_calls_ai_client_send():
+    """
+    Test that run_worker_lifecycle triggers the AI client and updates ticket status on success.
+    """
+    ticket = Ticket(id="T1", description="Task 1", status="todo", assigned_to="worker1")
+    context = WorkerContext(ticket_id="T1", model_name="test-model", messages=[])
+    
+    from multi_agent_conductor import run_worker_lifecycle
+    
+    with patch("ai_client.send") as mock_send:
+        mock_send.return_value = "Task complete. I have updated the file."
+        
+        result = run_worker_lifecycle(ticket, context)
+        
+        assert result == "Task complete. I have updated the file."
+        assert ticket.status == "completed"
+        mock_send.assert_called_once()
+        
+        # Check if description was passed to send()
+        args, kwargs = mock_send.call_args
+        # user_message is passed as a keyword argument
+        assert ticket.description in kwargs["user_message"]
+
+def test_run_worker_lifecycle_handles_blocked_response():
+    """
+    Test that run_worker_lifecycle marks the ticket as blocked if the AI indicates it cannot proceed.
+    """
+    ticket = Ticket(id="T1", description="Task 1", status="todo", assigned_to="worker1")
+    context = WorkerContext(ticket_id="T1", model_name="test-model", messages=[])
+    
+    from multi_agent_conductor import run_worker_lifecycle
+    
+    with patch("ai_client.send") as mock_send:
+        # Simulate a response indicating a block
+        mock_send.return_value = "I am BLOCKED because I don't have enough information."
+        
+        run_worker_lifecycle(ticket, context)
+        
+        assert ticket.status == "blocked"
+        assert "BLOCKED" in ticket.blocked_reason