Merge pull request microsoft#449 from microsoft/macae-v3-dev-marktayl

fix MStep agent name issue, fix json definitions

Author: Fr4nc3

data/agent_teams/hr.json

@@ -1,6 +1,6 @@
 {
-  "id": "3",
-  "team_id": "team-3",
+  "id": "1",
+  "team_id": "team-1",
   "name": "Human Resources Team",
   "status": "visible",
   "created": "",
@@ -10,15 +10,15 @@
       "input_key": "",
       "type": "",
       "name": "HRHelperAgent",
-      "deployment_name": "gpt-4.1",
+      "deployment_name": "gpt-4.1-mini",
       "icon": "",
       "system_message": "You have access to a number of HR related MCP tools for tasks like employee onboarding, benefits management, policy guidance, and general HR inquiries. Use these tools to assist employees with their HR needs efficiently and accurately.If you need more information to accurately call these tools, do not make up answers, call the ProxyAgent for clarification.",
       "description": "An agent that has access to various HR tools to assist employees with onboarding, benefits, policies, and general HR inquiries.",
       "use_rag": false,
       "use_mcp": true,
       "use_bing": false,
       "use_reasoning": false,
-      "index_name": "macae-index",
+      "index_name": "",
       "index_foundry_name": "",
       "index_endpoint": "",
       "coding_tools": false
@@ -27,30 +27,30 @@
       "input_key": "",
       "type": "",
       "name": "TechnicalSupportAgent",
-      "deployment_name": "gpt-4.1",
+      "deployment_name": "gpt-4.1-mini",
       "icon": "",
       "system_message": "You have access to a number of technical support MCP tools for tasks such as provisioning laptops, setting up email accounts, troubleshooting, software/hardware issues, and IT support. Use these tools to assist employees with their technical needs efficiently and accurately.  If you need more information to accurately call these tools, do not make up answers, call the ProxyAgent for clarification.",
       "description": "An agent that has access to various technical support tools to assist employees with IT needs like laptop provisioning, email setup, troubleshooting, and software/hardware issues.",
       "use_rag": false,
       "use_mcp": true,
       "use_bing": false,
       "use_reasoning": false,
-      "index_name": "macae-index",
+      "index_name": "",
       "index_foundry_name": "",
-      "coding_tools": true
+      "coding_tools": false
     },
     {
       "input_key": "",
       "type": "",
       "name": "ProxyAgent",
-      "deployment_name": "gpt-4.1",
+      "deployment_name": "",
       "icon": "",
       "system_message": "",
       "description": "",
       "use_rag": false,
       "use_mcp": false,
       "use_bing": false,
-      "use_reasoning": true,
+      "use_reasoning": false,
       "index_name": "",
       "index_foundry_name": "",
       "coding_tools": false

data/agent_teams/marketing.json

@@ -1,70 +1,70 @@
 {
-  "id": "1",
-  "team_id": "team-1",
+  "id": "2",
+  "team_id": "team-2",
   "name": "Product Marketing Team",
-  "status": "",
+  "status": "visible",
   "created": "",
   "created_by": "",
   "agents": [
     {
       "input_key": "",
       "type": "",
-      "name": "CustomerDataAgent",
-      "deployment_name": "gpt-4.1",
+      "name": "ProductAgent",
+      "deployment_name": "gpt-4.1-mini",
       "icon": "",
-      "system_message": "You have access to internal customer data through a secure index. Use this data to answer questions about customers, their interactions with customer service, satisfaction, etc. Be mindful of privacy and compliance regulations when handling customer data.",
-      "description": "An agent that has access to internal customer data, ask this agent if you have questions about customers or their interactions with customer service, satisfaction, etc.",
-      "use_rag": true,
-      "use_mcp": false,
+      "system_message": "You are a Product agent. You have knowledge about product management, development, and compliance guidelines. When asked to call a function, you should summarize back what was done.",
+      "description": "This agent specializes in product management, development, and related tasks. It can provide information about products, manage inventory, handle product launches, analyze sales data, and coordinate with other teams like marketing and tech support.",
+      "use_rag": false,
+      "use_mcp": true,
       "use_bing": false,
       "use_reasoning": false,
-      "index_name": "macae-index",
+      "index_name": "",
       "index_foundry_name": "",
       "index_endpoint": "",
       "coding_tools": false
     },
     {
       "input_key": "",
       "type": "",
-      "name": "OrderDataAgent",
-      "deployment_name": "gpt-4.1",
+      "name": "MarketingAgent",
+      "deployment_name": "gpt-4.1-mini",
       "icon": "",
-      "system_message": "You have access to internal order, inventory, product, and fulfilment data through a secure index. Use this data to answer questions about products, shipping delays, customer orders, warehouse management, etc. Be mindful of privacy and compliance regulations when handling customer data.",
-      "description": "An agent that has access to internal order, inventory, product, and fulfilment data. Ask this agent if you have questions about products, shipping delays, customerr orders, warehouse management, etc.",
-      "use_rag": true,
-      "use_mcp": false,
+      "system_message": "You are a Marketing agent. You specialize in marketing strategy, campaign development, content creation, and market analysis. You help create effective marketing campaigns, analyze market data, and develop promotional content for products and services.",
+      "description": "This agent specializes in marketing, campaign management, and analyzing market data.",
+      "use_rag": false,
+      "use_mcp": true,
       "use_bing": false,
       "use_reasoning": false,
-      "index_name": "macae-index",
+      "index_name": "",
       "index_foundry_name": "",
       "coding_tools": true
     },
     {
       "input_key": "",
       "type": "",
-      "name": "AnalysisRecomendationAgent",
-      "deployment_name": "o4-mini",
+      "name": "ProxyAgent",
+      "deployment_name": "gpt-4.1-mini",
       "icon": "",
-      "system_message": "You are a reasoning agent that can analyze customer data and provide recommendations for improving customer satisfaction and retention. You do not have access to any data sources, but you can reason based on the information provided to you by other agents. Use your reasoning skills to identify patterns, trends, and insights that can help improve customer satisfaction and retention. Provide actionable recommendations based on your analysis. You have access to other agents that can answer questions and provide data about customers, products, orders, inventory, and fulfilment. Use these agents to gather information as needed.",
-      "description": "A reasoning agent that can analyze customer data and provide recommendations for improving customer satisfaction and retention.",
+      "system_message": "",
+      "description": "",
       "use_rag": false,
       "use_mcp": false,
       "use_bing": false,
-      "use_reasoning": true,
+      "use_reasoning": false,
       "index_name": "",
       "index_foundry_name": "",
       "coding_tools": false
     }
   ],
   "protected": false,
-  "description": "Team focused on individualized customer relationship management and overall customer satisfaction.",
+  "description": "Team focused on products and product marketing.",
   "logo": "",
   "plan": "",
   "starting_tasks": [
     {
       "id": "task-1",
-      "name": "Satisfaction Plan",
-      "prompt": "Create a  plan to improve Emily Thompson's satisfaction.​",
+      "name": "Draft a press release",
+      "prompt": "Write a press release about our current products​",
       "created": "",
       "creator": "",
       "logo": ""

data/agent_teams/retail.json

@@ -10,7 +10,7 @@
       "input_key": "",
       "type": "",
       "name": "CustomerDataAgent",
-      "deployment_name": "gpt-4.1",
+      "deployment_name": "gpt-4.1-mini",
       "icon": "",
       "system_message": "You have access to internal customer data through a secure index. Use this data to answer questions about customers, their interactions with customer service, satisfaction, etc. Be mindful of privacy and compliance regulations when handling customer data.",
       "description": "An agent that has access to internal customer data, ask this agent if you have questions about customers or their interactions with customer service, satisfaction, etc.",
@@ -27,7 +27,7 @@
       "input_key": "",
       "type": "",
       "name": "OrderDataAgent",
-      "deployment_name": "gpt-4.1",
+      "deployment_name": "gpt-4.1-mini",
       "icon": "",
       "system_message": "You have access to internal order, inventory, product, and fulfilment data through a secure index. Use this data to answer questions about products, shipping delays, customer orders, warehouse management, etc. Be mindful of privacy and compliance regulations when handling customer data.",
       "description": "An agent that has access to internal order, inventory, product, and fulfilment data. Ask this agent if you have questions about products, shipping delays, customerr orders, warehouse management, etc.",

src/backend/app_kernel.py

@@ -2,7 +2,6 @@
 import asyncio
 import logging
 import os
-
 # Azure monitoring
 import re
 import uuid
@@ -13,40 +12,22 @@
 from azure.monitor.opentelemetry import configure_azure_monitor
 from common.config.app_config import config
 from common.database.database_factory import DatabaseFactory
-from common.models.messages_kernel import (
-    AgentMessage,
-    AgentType,
-    HumanClarification,
-    HumanFeedback,
-    InputTask,
-    Plan,
-    PlanStatus,
-    PlanWithSteps,
-    Step,
-    UserLanguage,
-)
+from common.models.messages_kernel import (AgentMessage, AgentType,
+                                           HumanClarification, HumanFeedback,
+                                           InputTask, Plan, PlanStatus,
+                                           PlanWithSteps, Step, UserLanguage)
 from common.utils.event_utils import track_event_if_configured
-
+from common.utils.utils_date import format_dates_in_messages
 # Updated import for KernelArguments
 from common.utils.utils_kernel import rai_success
-
 # FastAPI imports
-from fastapi import (
-    FastAPI,
-    HTTPException,
-    Query,
-    Request,
-    WebSocket,
-    WebSocketDisconnect,
-)
+from fastapi import (FastAPI, HTTPException, Query, Request, WebSocket,
+                     WebSocketDisconnect)
 from fastapi.middleware.cors import CORSMiddleware
 from kernel_agents.agent_factory import AgentFactory
-
 # Local imports
 from middleware.health_check import HealthCheckMiddleware
-from common.utils.utils_date import format_dates_in_messages
 from v3.api.router import app_v3
-
 # Semantic Kernel imports
 from v3.orchestration.orchestration_manager import OrchestrationManager
 

src/backend/v3/magentic_agents/proxy_agent.py

@@ -100,9 +100,7 @@ def __init__(self, user_id: str = None, **kwargs):
         effective_user_id = user_id or current_user_id.get() or ""
         super().__init__(
             name="ProxyAgent",
-            description="""Call this agent when you need to clarify requests by asking the human user 
-            for more information. Ask it for more details about any unclear requirements, missing information, 
-            or if you need the user to elaborate on any aspect of the task.""",
+            description="Call this agent when you need to clarify requests by asking the human user for more information. Ask it for more details about any unclear requirements, missing information, or if you need the user to elaborate on any aspect of the task.",
             user_id=effective_user_id,
             **kwargs
         )

src/backend/v3/models/models.py

@@ -16,18 +16,9 @@ class PlanStatus(str, Enum):
 
 class MStep(BaseModel):
     """model of a step in a plan"""
-    _agent: str = ""
+    agent: str = ""
     action: str = ""
 
-    @property
-    def agent(self):
-        return self._agent
-
-    @agent.setter
-    def agent(self, value):
-        self._agent = value if value is not None else ""
-
-
 class MPlan(BaseModel):
     """model of a plan"""
     id: str = Field(default_factory=lambda: str(uuid.uuid4()))

src/backend/v3/orchestration/human_approval_manager.py

@@ -12,7 +12,7 @@
 from semantic_kernel.agents.orchestration.magentic import (
     MagenticContext, StandardMagenticManager)
 from semantic_kernel.agents.orchestration.prompts._magentic_prompts import \
-    ORCHESTRATOR_TASK_LEDGER_PLAN_PROMPT
+    ORCHESTRATOR_TASK_LEDGER_FACTS_PROMPT
 from semantic_kernel.contents import ChatMessageContent
 from v3.config.settings import (connection_config, current_user_id,
                                 orchestration_config)
@@ -37,14 +37,12 @@ def __init__(self, *args, **kwargs):
         # object.__setattr__(self, 'current_user_id', None)
 
         custom_addition = """
-Before creating the final plan, please ask each agent - team member to list all relevant tools (including MCP tools, plug-ins, etc.) they have access to.  For each tool, list its required parameters.
-Obtain every required parameter that is not specified in the users request. These questions should be sent to the proxy agent.  Do not ask for information that is outside of this list of required parameters.
-
-Once this information is obtained, replan to create a final bullet-point plan to address the original request using the data and clarifications obtained.
-Each step in the plan should start with a specific agent which is responsible for executing it.
+As part of the plan, ask the team members regarding what relevant tools they have access to, and what information those tools require.  Please query the user through 
+the ProxyAgent if you need any additional information to supply required data to use these tools. Always clarify with the user if you are unsure about any aspect of 
+the request or the information you need to complete it.
 """
 
-        kwargs['task_ledger_plan_prompt'] = ORCHESTRATOR_TASK_LEDGER_PLAN_PROMPT + custom_addition
+        kwargs['task_ledger_facts_prompt'] = ORCHESTRATOR_TASK_LEDGER_FACTS_PROMPT + custom_addition
 
         super().__init__(*args, **kwargs)
 
@@ -66,6 +64,7 @@ async def plan(self, magentic_context: MagenticContext) -> Any:
         # First, let the parent create the actual plan
         print(" Creating execution plan...")
         plan = await super().plan(magentic_context)
+        print(f" Plan created: {plan}")
         self.magentic_plan = self.plan_to_obj( magentic_context, self.task_ledger)
 
         self.magentic_plan.user_id = current_user_id.get()
@@ -110,6 +109,11 @@ async def plan(self, magentic_context: MagenticContext) -> Any:
             #     content="Plan execution was cancelled by the user."
             # )
 
+    async def replan(self,magentic_context: MagenticContext) -> Any:
+        print(f"\nHuman-in-the-Loop Magentic Manager replanned:")
+        replan = await super().replan(magentic_context=magentic_context)
+        print(replan)
+        return replan
 
     async def _wait_for_user_approval(self, m_plan_id: Optional[str] = None) -> Optional[messages.PlanApprovalResponse]: # plan_id will not be optional in future
         """Wait for user approval response."""