feat(mma): Implement ConductorEngine and run_worker_lifecycle

multi_agent_conductor.py

@@ -0,0 +1,63 @@
+import ai_client
+from models import Ticket, Track, WorkerContext
+
+class ConductorEngine:
+    """
+    Orchestrates the execution of tickets within a track.
+    """
+    def __init__(self, track: Track):
+        self.track = track
+
+    def run_linear(self):
+        """
+        Executes tickets sequentially according to their dependencies.
+        Iterates through the track's executable tickets until no more can be run.
+        """
+        while True:
+            executable = self.track.get_executable_tickets()
+            if not executable:
+                # Check if we are finished or blocked
+                all_done = all(t.status == "completed" for t in self.track.tickets)
+                if all_done:
+                    print("Track completed successfully.")
+                else:
+                    print("No more executable tickets. Track may be blocked or finished.")
+                break
+            
+            for ticket in executable:
+                print(f"Executing ticket {ticket.id}: {ticket.description}")
+                # For now, we use a default model name or take it from config
+                context = WorkerContext(
+                    ticket_id=ticket.id, 
+                    model_name="gemini-2.5-flash-lite", 
+                    messages=[]
+                )
+                run_worker_lifecycle(ticket, context)
+
+def run_worker_lifecycle(ticket: Ticket, context: WorkerContext):
+    """
+    Simulates the lifecycle of a single agent working on a ticket.
+    Calls the AI client and updates the ticket status based on the response.
+    """
+    # Build a prompt for the worker
+    user_message = (
+        f"You are assigned to Ticket {ticket.id}.\n"
+        f"Task Description: {ticket.description}\n"
+        "Please complete this task. If you are blocked and cannot proceed, "
+        "start your response with 'BLOCKED' and explain why."
+    )
+    
+    # In a real scenario, we would pass md_content from the aggregator
+    # and manage the conversation history in the context.
+    response = ai_client.send(
+        md_content="", 
+        user_message=user_message,
+        base_dir="."
+    )
+    
+    if "BLOCKED" in response.upper():
+        ticket.mark_blocked(response)
+    else:
+        ticket.mark_complete()
+    
+    return response