Merge pull request #49831 from buzahid/sk-agents

Modules/M04-develop-ai-agent-with-semantic-kernel updated code sample

Author: JamesJBarnett

learn-pr/wwl-data-ai/develop-ai-agent-with-semantic-kernel/includes/3-create-azure-ai-agent.md

@@ -1,4 +1,4 @@
-**AzureAIAgent** is a specialized agent within the Semantic Kernel framework, designed to provide advanced conversational capabilities with seamless tool integration. It automates tool calling, eliminating the need for manual parsing and invocation. The agent also securely manages conversation history using threads, reducing the overhead of maintaining state. The AzureAIAgent class supports a variety of built-in tools, including file retrieval, code execution, and data interaction via Bing, Azure AI Search, Azure Functions, and OpenAPI.
+**AzureAIAgent** is a specialized agent within the Semantic Kernel framework, designed to provide advanced conversational capabilities with seamless tool integration. It automates tool calling, eliminating the need for manual parsing and invocation. The agent also securely manages conversation history using threads, reducing the overhead of maintaining state. The AzureAIAgent class supports many built-in tools, including file retrieval, code execution, and data interaction via Bing, Azure AI Search, Azure Functions, and OpenAPI.
 
 ## Creating an AzureAIAgent
 
@@ -12,11 +12,11 @@ To use an AzureAIAgent:
 1. Create an agent definition on the agent service provided by the client.
 1. Create an agent based on the definition.
 
-Here is the code that illustrates how to create an AzureAIAgent:
+Here's the code that illustrates how to create an AzureAIAgent:
 
 ```python
 from azure.identity.aio import DefaultAzureCredential
-from semantic_kernel.agents import AzureAIAgent, AzureAIAgentSettings
+from semantic_kernel.agents import AzureAIAgent, AzureAIAgentThread, AzureAIAgentSettings
 
 # Create an AzureAIAgentSettings object
 ai_agent_settings = AzureAIAgentSettings.create()
@@ -40,21 +40,23 @@ async with (@
     )
 ```
 
-Once your agent is defined, you can interact with your agent and invoke responses for inputs. To invoke responses, you create an agent thread and use the agent to add prompt and retrieve a response. For example:
+Once your agent is defined, you can create a thread to interact with your agent and invoke responses for inputs. For example:
 
 ```python
-thread: AzureAIAgentThread = AzureAIAgentThread()
+# Create the agent thread
+thread: AzureAIAgentThread = AzureAIAgentThread(client=client)
 
 try:
-    # Create a prompt 
-    prompt = "What are the largest semiconductor manufacturing companies?"
+    # Create prompts 
+    prompt_messages = ["What are the largest semiconductor manufacturing companies?"]
 
-    # Instruct the agent to add a message to the thread
-    await agent.add_chat_message(thread_id=thread.id, message=prompt)
+    # Invoke a response from the agent
+    response = await agent.get_response(messages=prompt_messages, thread_id=thread.id)
 
-    # Invoke the agent for response on the thread
-    response = await agent.get_response(thread_id=thread.id)
+    # View the response
+    print(response)
 finally:
+    # Clean up the thread
     await thread.delete() if thread else None
 ```
 
@@ -68,8 +70,8 @@ The Semantic Kernel AzureAIAgent object relies on the following components to fu
 
 - **Agent service** - the AzureAIAgent client also contains an agent operations service. This service helps streamline the process of creating, managing, and running the agents for your project.
 
-- **Agent definition** - the AzureAI Agent model created via the AzureAI Project client. This definition specifies the AI deployment model that should be used, as well as the name and instructions for the agent.
+- **Agent definition** - the AzureAI Agent model created via the AzureAI Project client. This definition specifies the AI deployment model that should be used, and the name and instructions for the agent.
 
-- **AzureAIAgentThread** - automatically maintains the conversation history between agents and users, as well as the state. You can add messages to a thread and use the agent to invoke a response from the LLM.
+- **AzureAIAgentThread** - automatically maintains the conversation history between agents and users, and the state. You can add messages to a thread and use the agent to invoke a response from the LLM.
 
 These components work together to allow you to create an agent with instructions to define its purpose and invoke responses from the AI model.