Merge branch 'main' into dev

Author: Abdul-Microsoft

.github/CODEOWNER .github/CODEOWNERS.github/CODEOWNER renamed to .github/CODEOWNERS

@@ -2,4 +2,4 @@
 # Each line is a file pattern followed by one or more owners.
 
 # These owners will be the default owners for everything in the repo.
-* @Avijit-Microsoft @Roopan-Microsoft @Prajwal-Microsoft @marktayl1 @Fr4nc3 
+* @Avijit-Microsoft @Roopan-Microsoft @Prajwal-Microsoft @marktayl1 @Fr4nc3 @Vinay-Microsoft @aniaroramsft

.github/workflows/deploy.yml

@@ -73,7 +73,7 @@ jobs:
         id: generate_rg_name
         run: |
           echo "Generating a unique resource group name..."
-          ACCL_NAME="ci-biab"  # Account name as specified
+          ACCL_NAME="macae"  # Account name as specified
           SHORT_UUID=$(uuidgen | cut -d'-' -f1)
           UNIQUE_RG_NAME="arg-${ACCL_NAME}-${SHORT_UUID}"
           echo "RESOURCE_GROUP_NAME=${UNIQUE_RG_NAME}" >> $GITHUB_ENV

README.md

@@ -95,8 +95,7 @@ Here are some example regions where the services are available: East US, East US
 
 Pricing varies per region and usage, so it isn't possible to predict exact costs for your usage. The majority of the Azure resources used in this infrastructure are on usage-based pricing tiers. However, Azure Container Registry has a fixed cost per registry per day.
 
-Use the [Azure pricing calculator](https://azure.microsoft.com/en-us/pricing/calculator) to calculate the cost of this solution in your subscription.
-
+Use the [Azure pricing calculator](https://azure.microsoft.com/en-us/pricing/calculator) to calculate the cost of this solution in your subscription. [Review a sample pricing sheet for the achitecture](https://azure.com/e/86d0eefbe4dd4a23981c1d3d4f6fe7ed).
 | Product | Description | Cost |
 |---|---|---|
 | [Azure OpenAI Service](https://learn.microsoft.com/azure/ai-services/openai/) | Powers the AI agents for task automation | [Pricing](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) |

azure.yaml

@@ -1,2 +1,4 @@
 # yaml-language-server: $schema=https://raw.githubusercontent.com/Azure/azure-dev/main/schemas/v1.0/azure.yaml.json
 name: multi-agent-custom-automation-engine-solution-accelerator
+metadata:
+  template: [email protected]

docs/images/readme/macae-architecture.png

@@ -70,7 +70,7 @@ var modelVersion = '2024-08-06'
 var aiServicesName = '${abbrs.ai.aiServices}${solutionPrefix}'
 var deploymentType = 'GlobalStandard'
 var gptModelVersion = 'gpt-4o'
-var appVersion = 'fnd01'
+var appVersion = 'latest'
 var resgistryName = 'biabcontainerreg'
 var dockerRegistryUrl = 'https://${resgistryName}.azurecr.io'
 

infra/main.bicep

@@ -6,7 +6,7 @@
     "_generator": {
       "name": "bicep",
       "version": "0.35.1.17967",
-      "templateHash": "5028939591915540887"
+      "templateHash": "6559422175999367984"
     }
   },
   "parameters": {
@@ -341,7 +341,7 @@
     "aiServicesName": "[format('{0}{1}', variables('abbrs').ai.aiServices, variables('solutionPrefix'))]",
     "deploymentType": "GlobalStandard",
     "gptModelVersion": "gpt-4o",
-    "appVersion": "fnd01",
+    "appVersion": "latest",
     "resgistryName": "biabcontainerreg",
     "dockerRegistryUrl": "[format('https://{0}.azurecr.io', variables('resgistryName'))]",
     "backendDockerImageURL": "[format('{0}.azurecr.io/macaebackend:{1}', variables('resgistryName'), variables('appVersion'))]",

infra/main.json

@@ -1,14 +1,13 @@
 # app_config.py
-import os
 import logging
-from typing import Optional, List
-from dotenv import load_dotenv
-from azure.identity import DefaultAzureCredential
-from azure.cosmos.aio import CosmosClient
+import os
+from typing import Optional
+
 from azure.ai.projects.aio import AIProjectClient
+from azure.cosmos.aio import CosmosClient
+from azure.identity import DefaultAzureCredential
+from dotenv import load_dotenv
 from semantic_kernel.kernel import Kernel
-from semantic_kernel.agents.azure_ai.azure_ai_agent import AzureAIAgent
-from semantic_kernel.functions import KernelFunction
 
 # Load environment variables from .env file
 load_dotenv()
@@ -188,94 +187,6 @@ def get_ai_project_client(self):
             logging.error("Failed to create AIProjectClient: %s", exc)
             raise
 
-    async def create_azure_ai_agent(
-        self,
-        agent_name: str,
-        instructions: str,
-        tools: Optional[List[KernelFunction]] = None,
-        client=None,
-        response_format=None,
-        temperature: float = 0.0,
-    ):
-        """
-        Creates a new Azure AI Agent with the specified name and instructions using AIProjectClient.
-        If an agent with the given name (assistant_id) already exists, it tries to retrieve it first.
-
-        Args:
-            kernel: The Semantic Kernel instance
-            agent_name: The name of the agent (will be used as assistant_id)
-            instructions: The system message / instructions for the agent
-            agent_type: The type of agent (defaults to "assistant")
-            tools: Optional tool definitions for the agent
-            tool_resources: Optional tool resources required by the tools
-            response_format: Optional response format to control structured output
-            temperature: The temperature setting for the agent (defaults to 0.0)
-
-        Returns:
-            A new AzureAIAgent instance
-        """
-        try:
-            # Get the AIProjectClient
-            if client is None:
-                client = self.get_ai_project_client()
-
-            # # ToDo: This is the fixed code but commenting it out as agent clean up is no happening yet
-            #  # and there are multiple versions of agents due to testing
-            # # First try to get an existing agent with this name as assistant_id
-            # try:
-            #     agent_id = None
-            #     agent_list = await client.agents.list_agents()
-            #     for agent in agent_list.data:
-            #         if agent.name == agent_name:
-            #             agent_id = agent.id
-            #             break
-            #             # If the agent already exists, we can use it directly
-            #             # Get the existing agent definition
-            #     existing_definition = await client.agents.get_agent(agent_id)
-            #     # Create the agent instance directly with project_client and existing definition
-            #     agent = AzureAIAgent(
-            #         client=client,
-            #         definition=existing_definition,
-            #         plugins=tools,
-            #     )
-
-            #     client.agents.list_agents()
-
-            #     return agent
-            # except Exception as e:
-            #     # The Azure AI Projects SDK throws an exception when the agent doesn't exist
-            #     # (not returning None), so we catch it and proceed to create a new agent
-            #     if "ResourceNotFound" in str(e) or "404" in str(e):
-            #         logging.info(
-            #             f"Agent with ID {agent_name} not found. Will create a new one."
-            #         )
-            #     else:
-            #         # Log unexpected errors but still try to create a new agent
-            #         logging.warning(
-            #             f"Unexpected error while retrieving agent {agent_name}: {str(e)}. Attempting to create new agent."
-            #         )
-
-            # Create the agent using the project client with the agent_name as both name and assistantId
-            agent_definition = await client.agents.create_agent(
-                model=self.AZURE_OPENAI_DEPLOYMENT_NAME,
-                name=agent_name,
-                instructions=instructions,
-                temperature=temperature,
-                response_format=response_format,
-            )
-
-            # Create the agent instance directly with project_client and definition
-            agent = AzureAIAgent(
-                client=client,
-                definition=agent_definition,
-                plugins=tools,
-            )
-
-            return agent
-        except Exception as exc:
-            logging.error("Failed to create Azure AI Agent: %s", exc)
-            raise
-
 
 # Create a global instance of AppConfig
 config = AppConfig()

src/backend/app_config.py

@@ -1,21 +1,15 @@
 import logging
-from typing import Any, List, Mapping, Optional
-
-from semantic_kernel.agents.azure_ai.azure_ai_agent import AzureAIAgent
-from semantic_kernel.functions import KernelFunction
-
+from abc import abstractmethod
+from typing import (Any, List, Mapping, Optional)
 
 # Import the new AppConfig instance
 from app_config import config
 from context.cosmos_memory_kernel import CosmosMemoryContext
 from event_utils import track_event_if_configured
-from models.messages_kernel import (
-    ActionRequest,
-    ActionResponse,
-    AgentMessage,
-    Step,
-    StepStatus,
-)
+from models.messages_kernel import (ActionRequest, ActionResponse,
+                                    AgentMessage, Step, StepStatus)
+from semantic_kernel.agents.azure_ai.azure_ai_agent import AzureAIAgent
+from semantic_kernel.functions import KernelFunction
 
 # Default formatting instructions used across agents
 DEFAULT_FORMATTING_INSTRUCTIONS = "Instructions: returning the output of this function call verbatim to the user in markdown. Then write AGENT SUMMARY: and then include a summary of what you did."
@@ -59,6 +53,7 @@ def __init__(
             endpoint=None,  # Set as needed
             api_version=None,  # Set as needed
             token=None,  # Set as needed
+            model=config.AZURE_OPENAI_DEPLOYMENT_NAME,
             agent_name=agent_name,
             system_prompt=system_message,
             client=client,
@@ -73,7 +68,7 @@ def __init__(
         self._tools = tools
         self._system_message = system_message
         self._chat_history = [{"role": "system", "content": self._system_message}]
-        self._agent = None  # Will be initialized in async_init
+        # self._agent = None  # Will be initialized in async_init
 
         # Required properties for AgentGroupChat compatibility
         self.name = agent_name  # This is crucial for AgentGroupChat to identify agents
@@ -91,24 +86,6 @@ def default_system_message(agent_name=None) -> str:
         name = agent_name
         return f"You are an AI assistant named {name}. Help the user by providing accurate and helpful information."
 
-    async def async_init(self):
-        """Asynchronously initialize the agent after construction.
-
-        This method must be called after creating the agent to complete initialization.
-        """
-        logging.info(f"Initializing agent: {self._agent_name}")
-        # Create Azure AI Agent or fallback
-        if not self._agent:
-            self._agent = await config.create_azure_ai_agent(
-                agent_name=self._agent_name,
-                instructions=self._system_message,
-                tools=self._tools,
-            )
-        else:
-            logging.info(f"Agent {self._agent_name} already initialized.")
-        # Tools are registered with the kernel via get_tools_from_config
-        return self
-
     async def handle_action_request(self, action_request: ActionRequest) -> str:
         """Handle an action request from another agent or the system.
 
@@ -154,7 +131,7 @@ async def handle_action_request(self, action_request: ActionRequest) -> str:
             # thread = self.client.agents.get_thread(
             #     thread=step.session_id
             # )  # AzureAIAgentThread(thread_id=step.session_id)
-            async_generator = self._agent.invoke(
+            async_generator = self.invoke(
                 messages=f"{str(self._chat_history)}\n\nPlease perform this action",
                 thread=thread,
             )
@@ -258,3 +235,83 @@ def save_state(self) -> Mapping[str, Any]:
     def load_state(self, state: Mapping[str, Any]) -> None:
         """Load the state of this agent."""
         self._memory_store.load_state(state["memory"])
+
+    @classmethod
+    @abstractmethod
+    async def create(cls, **kwargs) -> "BaseAgent":
+        """Create an instance of the agent."""
+        pass
+
+    @staticmethod
+    async def _create_azure_ai_agent_definition(
+        agent_name: str,
+        instructions: str,
+        tools: Optional[List[KernelFunction]] = None,
+        client=None,
+        response_format=None,
+        temperature: float = 0.0,
+    ):
+        """
+        Creates a new Azure AI Agent with the specified name and instructions using AIProjectClient.
+        If an agent with the given name (assistant_id) already exists, it tries to retrieve it first.
+
+        Args:
+            kernel: The Semantic Kernel instance
+            agent_name: The name of the agent (will be used as assistant_id)
+            instructions: The system message / instructions for the agent
+            agent_type: The type of agent (defaults to "assistant")
+            tools: Optional tool definitions for the agent
+            tool_resources: Optional tool resources required by the tools
+            response_format: Optional response format to control structured output
+            temperature: The temperature setting for the agent (defaults to 0.0)
+
+        Returns:
+            A new AzureAIAgent definition or an existing one if found
+        """
+        try:
+            # Get the AIProjectClient
+            if client is None:
+                client = config.get_ai_project_client()
+
+            # # First try to get an existing agent with this name as assistant_id
+            try:
+                agent_id = None
+                agent_list = await client.agents.list_agents()
+                for agent in agent_list.data:
+                    if agent.name == agent_name:
+                        agent_id = agent.id
+                        break
+                # If the agent already exists, we can use it directly
+                # Get the existing agent definition
+                if agent_id is not None:
+                    logging.info(f"Agent with ID {agent_id} exists.")
+
+                    existing_definition = await client.agents.get_agent(agent_id)
+
+                    return existing_definition
+            except Exception as e:
+                # The Azure AI Projects SDK throws an exception when the agent doesn't exist
+                # (not returning None), so we catch it and proceed to create a new agent
+                if "ResourceNotFound" in str(e) or "404" in str(e):
+                    logging.info(
+                        f"Agent with ID {agent_name} not found. Will create a new one."
+                    )
+                else:
+                    # Log unexpected errors but still try to create a new agent
+                    logging.warning(
+                        f"Unexpected error while retrieving agent {agent_name}: {str(e)}. Attempting to create new agent."
+                    )
+
+            # Create the agent using the project client with the agent_name as both name and assistantId
+            agent_definition = await client.agents.create_agent(
+                model=config.AZURE_OPENAI_DEPLOYMENT_NAME,
+                name=agent_name,
+                instructions=instructions,
+                temperature=temperature,
+                response_format=response_format,
+            )
+
+            return agent_definition
+        except Exception as exc:
+            logging.error("Failed to create Azure AI Agent: %s", exc)
+            raise