fix for agent multiple times calling

Author: Abdul-Microsoft

src/backend/v4/magentic_agents/foundry_agent.py

@@ -185,12 +185,12 @@ 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"[DEBUG] Creating agent '{self.agent_name}' with instructions (first 200 chars): {enhanced_instructions[:200]}...")
-            print(f"[DEBUG] Agent model: {self.model_deployment_name}")
-            print(f"[DEBUG] Search config: connection={connection_name}, index={index_name}, query_type={query_type}, top_k={top_k}")
+            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,
+                agent_name=self.agent_name,  # Use original name
                 definition=PromptAgentDefinition(
                     model=self.model_deployment_name,
                     instructions=enhanced_instructions,
@@ -213,19 +213,13 @@ 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, connection=%s, index=%s, query_type=%s, top_k=%s).",
+                "Created Azure AI Search agent via create_version (name=%s, id=%s, version=%s).",
                 azure_agent.name,
                 azure_agent.id,
                 azure_agent.version,
-                connection_name,
-                index_name,
-                query_type,
-                top_k,
             )
-            print(f"[DEBUG] Created agent via create_version: name={azure_agent.name}, id={azure_agent.id}, version={azure_agent.version}")
-            print(f"[DEBUG] Agent definition: {azure_agent.definition}")
-            print(f"[DEBUG] Agent instructions from definition: {getattr(azure_agent.definition, 'instructions', 'N/A')}")
 
             # Wrap in AzureAIClient using agent_name and agent_version (NOT agent_id)
             # AzureAIClient constructor: agent_name, agent_version, project_endpoint, credential

src/backend/v4/magentic_agents/magentic_agent_factory.py

@@ -115,6 +115,7 @@ 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

@@ -33,6 +33,7 @@ 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):
         """
@@ -43,6 +44,9 @@ 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 = """
 
@@ -55,13 +59,23 @@ def __init__(self, user_id: str, agent, *args, **kwargs):
 to be taken. If a step involves multiple actions, separate them into distinct steps with an agent included in each step.
 If the step is taken by an agent that is not part of the team, such as the MagenticManager, please always list the MagenticManager as the agent for that step. At any time, if more information is needed from the user, use the ProxyAgent to request this information.
 
+CRITICAL: Each agent should only be called ONCE to perform their task. Do NOT call the same agent multiple times.
+After an agent has provided their response, move on to the next agent in the plan.
+
 Here is an example of a well-structured plan:
 - **EnhancedResearchAgent** to gather authoritative data on the latest industry trends and best practices in employee onboarding
 - **EnhancedResearchAgent** to gather authoritative data on Innovative onboarding techniques that enhance new hire engagement and retention.
 - **DocumentCreationAgent** to draft a comprehensive onboarding plan that includes a detailed schedule of onboarding activities and milestones.
 - **DocumentCreationAgent** to draft a comprehensive onboarding plan that includes a checklist of resources and materials needed for effective onboarding.
 - **ProxyAgent** to review the drafted onboarding plan for clarity and completeness.
 - **MagenticManager** to finalize the onboarding plan and prepare it for presentation to stakeholders.
+"""
+
+        # Add progress ledger prompt to prevent re-calling agents
+        progress_append = """
+CRITICAL RULE: DO NOT call the same agent more than once unless absolutely necessary.
+If an agent has already provided a response, consider their task COMPLETE and move to the next agent.
+Only re-call an agent if their previous response was explicitly an error or failure.
 """
 
         final_append = """
@@ -75,6 +89,10 @@ def __init__(self, user_id: str, agent, *args, **kwargs):
             ORCHESTRATOR_TASK_LEDGER_PLAN_UPDATE_PROMPT + plan_append
         )
         kwargs["final_answer_prompt"] = ORCHESTRATOR_FINAL_ANSWER_PROMPT + final_append
+        
+        # Override progress ledger prompt to discourage re-calling agents
+        from agent_framework._workflows._magentic import ORCHESTRATOR_PROGRESS_LEDGER_PROMPT
+        kwargs["progress_ledger_prompt"] = ORCHESTRATOR_PROGRESS_LEDGER_PROMPT + progress_append
 
         self.current_user_id = user_id
         # New API: StandardMagenticManager takes agent as first positional argument
@@ -305,4 +323,4 @@ def plan_to_obj(self, magentic_context: MagenticContext, ledger) -> MPlan:
             task=task_text,
         )
 
-        return return_plan
+        return return_plan

src/backend/v4/orchestration/orchestration_manager.py

@@ -197,17 +197,19 @@ async def init_orchestration(
         # Assemble workflow with callback
         storage = InMemoryCheckpointStorage()
 
-        # New API: .participants() accepts a list of agents
+        # New SDK: participants() accepts a Sequence (list) of agents
+        # 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()
-            .participants(participant_list)
+            .participants(participant_list)  # New SDK: pass as list
             .with_manager(
                 manager=manager,  # Pass manager instance (extends StandardMagenticManager)
                 max_round_count=orchestration_config.max_rounds,
-                max_stall_count=3,
-                max_reset_count=2,
+                max_stall_count=0,  # CRITICAL: Prevent re-calling agents when stalled (default is 3!)
             )
             .with_checkpointing(storage)
         )
@@ -381,12 +383,16 @@ async def run_orchestration(self, user_id: str, input_task) -> None:
         task_text = getattr(input_task, "description", str(input_task))
         self.logger.debug("Task: %s", task_text)
 
+        # Track how many times each agent is called (for debugging duplicate calls)
+        agent_call_counts: dict = {}
+
         try:
             # Execute workflow using run_stream with task as positional parameter
             # The execution settings are configured in the manager/client
             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:
@@ -431,11 +437,20 @@ async def run_orchestration(self, user_id: str, input_task) -> None:
 
                     # Handle group chat request sent
                     elif isinstance(event, GroupChatRequestSentEvent):
+                        agent_name = event.participant_name
+                        agent_call_counts[agent_name] = agent_call_counts.get(agent_name, 0) + 1
+                        call_num = agent_call_counts[agent_name]
+                        
                         self.logger.info(
-                            "[REQUEST SENT (round %d)] to agent: %s",
+                            "[REQUEST SENT (round %d)] to agent: %s (call #%d)",
                             event.round_index,
-                            event.participant_name
+                            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)
 
                     # Handle group chat response received - THIS IS WHERE AGENT RESPONSES COME
                     elif isinstance(event, GroupChatResponseReceivedEvent):
@@ -496,6 +511,13 @@ async def run_orchestration(self, user_id: str, input_task) -> None:
             # Extract final result
             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
             self.logger.info("\nAgent responses:")
             self.logger.info(