feat(mma): Implement spawn interception in multi_agent_conductor.py

multi_agent_conductor.py

@@ -1,11 +1,12 @@
 import ai_client
 import json
 import asyncio
-from typing import List, Optional
+from typing import List, Optional, Tuple
 from dataclasses import asdict
 import events
 from models import Ticket, Track, WorkerContext
 from file_cache import ASTParser
+from pathlib import Path
 
 from dag_engine import TrackDAG, ExecutionEngine
 
@@ -181,6 +182,57 @@ def confirm_execution(payload: str, event_queue: events.AsyncEventQueue, ticket_
     
     return False
 
+def confirm_spawn(role: str, prompt: str, context_md: str, event_queue: events.AsyncEventQueue, ticket_id: str) -> Tuple[bool, str, str]:
+    """
+    Pushes a spawn approval request to the GUI and waits for response.
+    Returns (approved, modified_prompt, modified_context)
+    """
+    import threading
+    import time
+    import asyncio
+    
+    dialog_container = [None]
+    
+    task = {
+        "action": "mma_spawn_approval",
+        "ticket_id": ticket_id,
+        "role": role,
+        "prompt": prompt,
+        "context_md": context_md,
+        "dialog_container": dialog_container
+    }
+    
+    # Push to queue
+    try:
+        loop = asyncio.get_event_loop()
+        if loop.is_running():
+            asyncio.run_coroutine_threadsafe(event_queue.put("mma_spawn_approval", task), loop)
+        else:
+            event_queue._queue.put_nowait(("mma_spawn_approval", task))
+    except Exception:
+        # Fallback if no loop
+        event_queue._queue.put_nowait(("mma_spawn_approval", task))
+
+    # Wait for the GUI to create the dialog and for the user to respond
+    start = time.time()
+    while dialog_container[0] is None and time.time() - start < 60:
+        time.sleep(0.1)
+    
+    if dialog_container[0]:
+        approved, final_payload = dialog_container[0].wait()
+        
+        # Extract modifications from final_payload if it's a dict
+        modified_prompt = prompt
+        modified_context = context_md
+        
+        if isinstance(final_payload, dict):
+            modified_prompt = final_payload.get("prompt", prompt)
+            modified_context = final_payload.get("context_md", context_md)
+            
+        return approved, modified_prompt, modified_context
+    
+    return False, prompt, context_md
+
 def run_worker_lifecycle(ticket: Ticket, context: WorkerContext, context_files: List[str] = None, event_queue: events.AsyncEventQueue = None, engine: Optional['ConductorEngine'] = None, md_content: str = ""):
     """
     Simulates the lifecycle of a single agent working on a ticket.
@@ -202,10 +254,7 @@ def run_worker_lifecycle(ticket: Ticket, context: WorkerContext, context_files:
         for i, file_path in enumerate(context_files):
             try:
                 abs_path = Path(file_path)
-                if not abs_path.is_absolute() and engine:
-                    # Resolve relative to project base if possible
-                    # (This is a bit simplified, but helps)
-                    pass
+                # (This is a bit simplified, but helps)
                 with open(file_path, 'r', encoding='utf-8') as f:
                     content = f.read()
                 if i == 0:
@@ -229,6 +278,22 @@ def run_worker_lifecycle(ticket: Ticket, context: WorkerContext, context_files:
         "start your response with 'BLOCKED' and explain why."
     )
     
+    # HITL Clutch: call confirm_spawn if event_queue is provided
+    if event_queue:
+        approved, modified_prompt, modified_context = confirm_spawn(
+            role="Tier 3 Worker",
+            prompt=user_message,
+            context_md=md_content,
+            event_queue=event_queue,
+            ticket_id=ticket.id
+        )
+        if not approved:
+            ticket.mark_blocked("Spawn rejected by user.")
+            return "BLOCKED: Spawn rejected by user."
+        
+        user_message = modified_prompt
+        md_content = modified_context
+
     # HITL Clutch: pass the queue and ticket_id to confirm_execution
     def clutch_callback(payload: str) -> bool:
         if not event_queue:
@@ -246,9 +311,6 @@ def run_worker_lifecycle(ticket: Ticket, context: WorkerContext, context_files:
     # Update usage in engine if provided
     if engine:
         stats = {} # ai_client.get_token_stats() is not available
-        # ai_client provides aggregate stats, for granular tier tracking 
-        # we'd need to diff before/after or have ai_client return usage per call.
-        # For Phase 4, we'll use a simplified diff approach.
         engine.tier_usage["Tier 3"]["input"] += stats.get("prompt_tokens", 0)
         engine.tier_usage["Tier 3"]["output"] += stats.get("candidates_tokens", 0)