Refactor FoundryAgentTemplate and MagenticAgentFactory to remove debug print statements and streamline logging

Author: Abdul-Microsoft

src/backend/v4/magentic_agents/foundry_agent.py

@@ -70,23 +70,18 @@ def __init__(
         self._use_azure_search = self._is_azure_search_requested()
         self.use_reasoning = use_reasoning
 
-        # Placeholder for server-created Azure AI agent id/version (if Azure Search path)
+        # Placeholder for server-created Azure AI agent id (if Azure Search path)
         self._azure_server_agent_id: Optional[str] = None
-        self._azure_server_agent_version: Optional[str] = None
 
     # -------------------------
     # Mode detection
     # -------------------------
     def _is_azure_search_requested(self) -> bool:
         """Determine if Azure AI Search raw tool path should be used."""
-        print(f"[DEBUG _is_azure_search_requested] Agent={self.agent_name}, search={self.search}")
         if not self.search:
-            print(f"[DEBUG _is_azure_search_requested] Agent={self.agent_name}: No search config, returning False")
             return False
         # Minimal heuristic: presence of required attributes
-
         has_index = hasattr(self.search, "index_name") and bool(self.search.index_name)
-        print(f"[DEBUG _is_azure_search_requested] Agent={self.agent_name}: has_index={has_index}, index_name={getattr(self.search, 'index_name', None)}")
         if has_index:
             self.logger.info(
                 "Azure AI Search requested (connection_id=%s, index=%s).",
@@ -137,7 +132,6 @@ async def _create_azure_search_enabled_client(self, chatClient=None) -> Optional
         Returns:
             AzureAIClient | None
         """
-        print(f"[DEBUG _create_azure_search_enabled_client] Agent={self.agent_name}, chatClient={chatClient}, search_config={self.search}")
         if chatClient:
             self.logger.info("Reusing existing chatClient for agent '%s' (already has Azure Search configured)", self.agent_name)
             return chatClient
@@ -185,9 +179,6 @@ async def _create_azure_search_enabled_client(self, chatClient=None) -> Optional
                 "Always use the Azure AI Search tool and configured index for knowledge retrieval."
             )
 
-            print(f"[AGENT CREATE] 🆕 Creating agent in Foundry: '{self.agent_name}'", flush=True)
-            print(f"[AGENT CREATE] Model: {self.model_deployment_name}", flush=True)
-            print(f"[AGENT CREATE] Search: connection={connection_name}, index={index_name}", flush=True)
 
             azure_agent = await self.project_client.agents.create_version(
                 agent_name=self.agent_name,  # Use original name
@@ -212,8 +203,7 @@ async def _create_azure_search_enabled_client(self, chatClient=None) -> Optional
             )
 
             self._azure_server_agent_id = azure_agent.id
-            self._azure_server_agent_version = azure_agent.version
-            print(f"[AGENT CREATE] ✅ Created agent: name={azure_agent.name}, id={azure_agent.id}, version={azure_agent.version}", flush=True)
+
             self.logger.info(
                 "Created Azure AI Search agent via create_version (name=%s, id=%s, version=%s).",
                 azure_agent.name,
@@ -239,8 +229,6 @@ async def _create_azure_search_enabled_client(self, chatClient=None) -> Optional
                 index_name,
                 ex,
             )
-            import traceback
-            traceback.print_exc()
             return None
 
     # -------------------------
@@ -257,7 +245,6 @@ async def _after_open(self) -> None:
 
         try:
             chatClient = await self.get_database_team_agent()
-            print(f"[DEBUG _after_open] Agent={self.agent_name}, _use_azure_search={self._use_azure_search}, search_config={self.search}, chatClient={chatClient}")
 
             if self._use_azure_search:
                 # Azure Search mode (skip MCP + Code Interpreter due to incompatibility)
@@ -266,7 +253,6 @@ async def _after_open(self) -> None:
                     self.agent_name,
                     getattr(self.search, "index_name", "N/A") if self.search else "N/A"
                 )
-                print(f"[DEBUG _after_open] Creating Azure Search client for {self.agent_name}")
                 chat_client = await self._create_azure_search_enabled_client(chatClient)
                 if not chat_client:
                     raise RuntimeError(

src/backend/v4/magentic_agents/magentic_agent_factory.py

@@ -115,7 +115,6 @@ async def create_agent_from_config(
             index_name,
             "Reasoning" if use_reasoning else "Foundry",
         )
-        print(f"[FACTORY] 🆕 Creating NEW agent: {agent_obj.name} (id={id(agent_obj)})", flush=True)
 
         agent = FoundryAgentTemplate(
             agent_name=agent_obj.name,

src/backend/v4/orchestration/human_approval_manager.py

@@ -33,7 +33,6 @@ class HumanApprovalMagenticManager(StandardMagenticManager):
     approval_enabled: bool = True
     magentic_plan: Optional[MPlan] = None
     current_user_id: str  # populated in __init__
-    _called_agents: set  # Track which agents have been called
 
     def __init__(self, user_id: str, agent, *args, **kwargs):
         """
@@ -44,9 +43,6 @@ def __init__(self, user_id: str, agent, *args, **kwargs):
             *args: Additional positional arguments for the parent StandardMagenticManager.
             **kwargs: Additional keyword arguments for the parent StandardMagenticManager.
         """
-        
-        # Initialize called agents tracker
-        self._called_agents = set()
 
         plan_append = """
 

src/backend/v4/orchestration/orchestration_manager.py

@@ -202,7 +202,6 @@ async def init_orchestration(
         # The orchestrator uses agent.name to identify them
         participant_list = list(participants.values())
         cls.logger.info("Participants for workflow: %s", list(participants.keys()))
-        print(f"[DEBUG] Participants for workflow: {list(participants.keys())}", flush=True)
 
         builder = (
             MagenticBuilder()
@@ -277,24 +276,17 @@ async def get_current_or_new_orchestration(
                 cls.logger.error(
                     "Failed to create agents for user '%s': %s", user_id, e
                 )
-                print(f"Failed to create agents for user '{user_id}': {e}")
                 raise
             try:
                 cls.logger.info("Initializing new orchestration for user '%s'", user_id)
-                print(f"[DEBUG] Initializing new orchestration for user '{user_id}'")
                 workflow = await cls.init_orchestration(
                     agents, team_config, team_service.memory_context, user_id
                 )
                 orchestration_config.orchestrations[user_id] = workflow
-                print(f"[DEBUG] Stored workflow for user '{user_id}': {workflow is not None}")
-                print(f"[DEBUG] orchestrations keys: {list(orchestration_config.orchestrations.keys())}")
             except Exception as e:
                 cls.logger.error(
                     "Failed to initialize orchestration for user '%s': %s", user_id, e
                 )
-                print(f"Failed to initialize orchestration for user '{user_id}': {e}")
-                import traceback
-                traceback.print_exc()
                 raise
         return orchestration_config.get_current_orchestration(user_id)
 
@@ -310,13 +302,9 @@ async def run_orchestration(self, user_id: str, input_task) -> None:
         self.logger.info(
             "Starting orchestration job '%s' for user '%s'", job_id, user_id
         )
-        print(f"[DEBUG] run_orchestration called for user '{user_id}'")
-        print(f"[DEBUG] orchestrations keys before get: {list(orchestration_config.orchestrations.keys())}")
 
         workflow = orchestration_config.get_current_orchestration(user_id)
-        print(f"[DEBUG] workflow is None: {workflow is None}")
         if workflow is None:
-            print(f"[ERROR] Orchestration not initialized for user '{user_id}'")
             raise ValueError("Orchestration not initialized for user.")
         # Fresh thread per participant to avoid cross-run state bleed
         executors = getattr(workflow, "executors", {})
@@ -393,7 +381,7 @@ async def run_orchestration(self, user_id: str, input_task) -> None:
             final_output: str | None = None
 
             self.logger.info("Starting workflow execution...")
-            print(f"[ORCHESTRATOR] 🚀 Starting workflow with max_rounds={orchestration_config.max_rounds}", flush=True)
+
             last_message_id: str | None = None
             async for event in workflow.run_stream(task_text):
                 try:
@@ -448,10 +436,9 @@ async def run_orchestration(self, user_id: str, input_task) -> None:
                             agent_name,
                             call_num
                         )
-                        print(f"[ORCHESTRATOR] 📤 REQUEST SENT round={event.round_index} to agent={agent_name} (call #{call_num})", flush=True)
 
                         if call_num > 1:
-                            print(f"[ORCHESTRATOR] ⚠️ WARNING: Agent '{agent_name}' called {call_num} times!", flush=True)
+                            self.logger.warning("Agent '%s' called %d times", agent_name, call_num)
 
                     # Handle group chat response received - THIS IS WHERE AGENT RESPONSES COME
                     elif isinstance(event, GroupChatResponseReceivedEvent):
@@ -513,10 +500,6 @@ async def run_orchestration(self, user_id: str, input_task) -> None:
             final_text = final_output if final_output else ""
 
             # Log agent call summary
-            print(f"\n[ORCHESTRATOR] 📊 AGENT CALL SUMMARY:", flush=True)
-            for agent_name, count in agent_call_counts.items():
-                status = "✅" if count == 1 else "⚠️ DUPLICATE"
-                print(f"  {status} {agent_name}: called {count} time(s)", flush=True)
             self.logger.info("Agent call counts: %s", agent_call_counts)
 
             # Log results