Merge pull request #53597 from buzahid/ai-agents

Updated exercise link, content freshness reviews for ai modules.

Author: Stacyrch140

learn-pr/wwl-data-ai/build-agent-with-custom-tools/1-introduction.yml

@@ -4,7 +4,7 @@ title: Introduction
 metadata:
   title: Introduction
   description: "Introduction to building an agent with custom tools."
-  ms.date: 05/23/2025
+  ms.date: 02/24/2026
   author: ivorb
   ms.author: berryivor
   ms.topic: unit

learn-pr/wwl-data-ai/build-agent-with-custom-tools/2-why-use-custom-tools.yml

@@ -4,7 +4,7 @@ title: Why use custom tools
 metadata:
   title: Why use custom tools
   description: "Understand the benefits of using custom tools with your agent."
-  ms.date: 05/23/2025
+  ms.date: 02/24/2026
   author: ivorb
   ms.author: berryivor
   ms.topic: unit

learn-pr/wwl-data-ai/build-agent-with-custom-tools/3-custom-tool-options.yml

@@ -4,7 +4,7 @@ title: Options for implementing custom tools
 metadata:
   title: Options for implementing custom tools
   description: "Explore the options for implementing custom tools in your agent."
-  ms.date: 05/23/2025
+  ms.date: 02/24/2026
   author: ivorb
   ms.author: berryivor
   ms.topic: unit

learn-pr/wwl-data-ai/build-agent-with-custom-tools/4-how-use-custom-tools.yml

@@ -4,7 +4,7 @@ title: How to integrate custom tools
 metadata:
   title: How to integrate custom tools
   description: "Learn how to integrate custom tools into your agent. This unit covers function calling, Azure Function integration, and OpenAPI specification integration."
-  ms.date: 05/23/2025
+  ms.date: 02/24/2026
   author: ivorb
   ms.author: berryivor
   ms.topic: unit

learn-pr/wwl-data-ai/build-agent-with-custom-tools/5-exercise.yml

@@ -4,7 +4,7 @@ title: Exercise - Build an agent with custom tools
 metadata:
   title: Exercise - Build an agent with custom tools
   description: "Get interactive experience with building an agent with custom tools."
-  ms.date: 05/23/2025
+  ms.date: 02/24/2026
   author: ivorb
   ms.author: berryivor
   ms.topic: unit

learn-pr/wwl-data-ai/build-agent-with-custom-tools/6-knowledge-check.yml

@@ -4,7 +4,7 @@ title: Module assessment
 metadata:
   title: Module assessment
   description: "Check your knowledge of building an agent with custom tools."
-  ms.date: 05/23/2025
+  ms.date: 02/24/2026
   author: ivorb
   ms.author: berryivor
   ms.topic: unit

learn-pr/wwl-data-ai/build-agent-with-custom-tools/7-summary.yml

@@ -4,7 +4,7 @@ title: Summary
 metadata:
   title: Summary
   description: "Review the key points of using custom tools with your agent."
-  ms.date: 05/23/2025
+  ms.date: 02/24/2026
   author: ivorb
   ms.author: berryivor
   ms.topic: unit

learn-pr/wwl-data-ai/build-agent-with-custom-tools/includes/4-how-use-custom-tools.md

@@ -20,27 +20,37 @@ def recent_snowfall(location: str) -> str:
     mock_snow_data = {"Seattle": "0 inches", "Denver": "2 inches"}
     snow = mock_snow_data.get(location, "Data not available.")
     return json.dumps({"location": location, "snowfall": snow})
-
-user_functions: Set[Callable[..., Any]] = {
-    recent_snowfall,
-}
 ```
 
 Register the function with your agent using the Azure AI SDK:
 
 ```python
-# Initialize agent toolset with user functions
-functions = FunctionTool(user_functions)
-toolset = ToolSet()
-toolset.add(functions)
-agent_client.enable_auto_function_calls(toolset=toolset)
+# Define a function tool for the model to use
+function_tool = FunctionTool(
+    name="recent_snowfall",
+    parameters={
+        "type": "object",
+        "properties": {
+            "location": {"type": "string", "description": "The city name to check snowfall for."},
+        },
+        "required": ["location"],
+        "additionalProperties": False
+    },
+    description="Get recent snowfall totals for a given location.",
+    strict=True,
+)
+
+# Add the function tool to a list of tools for the agent
+tools: list[Tool] = [function_tool]
 
 # Create your agent with the toolset
-agent = agent_client.create_agent(
-    model="gpt-4o-mini",
+agent = project_client.agents.create_version(
     name="snowfall-agent",
-    instructions="You are a weather assistant tracking snowfall. Use the provided functions to answer questions.",
-    toolset=toolset
+    definition=PromptAgentDefinition(
+        model="gpt-4.1",
+        instructions="You are a weather assistant tracking snowfall. Use the provided functions to answer questions.",
+        tools=tools,
+    )
 )
 ```
 
@@ -57,33 +67,38 @@ First, develop and deploy your Azure Function. In this example, imagine we have
 When your Azure Function is in place, integrate add it to the agent definition as an Azure Function tool:
 
   ```python
-  storage_service_endpoint = "https://<your-storage>.queue.core.windows.net"
-  
-  azure_function_tool = AzureFunctionTool(
-      name="get_snowfall",
-      description="Get snowfall information using Azure Function",
-      parameters={
-              "type": "object",
-              "properties": {
-                  "location": {"type": "string", "description": "The location to check snowfall."},
+  tool = AzureFunctionTool(
+      azure_function=AzureFunctionDefinition(
+          input_binding=AzureFunctionBinding(
+              storage_queue=AzureFunctionStorageQueue(
+                  queue_name="STORAGE_INPUT_QUEUE_NAME",
+                  queue_service_endpoint="STORAGE_QUEUE_SERVICE_ENDPOINT",
+              )
+          ),
+          output_binding=AzureFunctionBinding(
+              storage_queue=AzureFunctionStorageQueue(
+                  queue_name="STORAGE_OUTPUT_QUEUE_NAME",
+                  queue_service_endpoint="STORAGE_QUEUE_SERVICE_ENDPOINT",
+              )
+          ),
+          function=AzureFunctionDefinitionFunction(
+              name="queue_trigger",
+              description="Get weather for a given location",
+              parameters={
+                  "type": "object",
+                  "properties": {"location": {"type": "string", "description": "location to determine weather for"}},
               },
-              "required": ["location"],
-          },
-      input_queue=AzureFunctionStorageQueue(
-          queue_name="input",
-          storage_service_endpoint=storage_service_endpoint,
-      ),
-      output_queue=AzureFunctionStorageQueue(
-          queue_name="output",
-          storage_service_endpoint=storage_service_endpoint,
-      ),
+          ),
+      )
   )
-  
-  agent = agent_client.create_agent(
-      model=os.environ["MODEL_DEPLOYMENT_NAME"],
-      name="azure-function-agent",
-      instructions="You are a snowfall tracking agent. Use the provided Azure Function to fetch snowfall based on location.",
-      tools=azure_function_tool.definitions,
+
+  agent = project_client.agents.create_version(
+      agent_name="MyAgent",
+      definition=PromptAgentDefinition(
+          model="gpt-4.1",
+          instructions="You are a helpful weather assistant. Use the provided Azure Function to get weather information for a location when needed.",
+          tools=[tool],
+      ),
   )
   ```
 
@@ -98,71 +113,101 @@ OpenAPI defined tools allow agents to interact with external APIs using standard
 
 ### Example: Using an OpenAPI specification
 
-First, create a JSON file ( in this example, called *snowfall_openapi.json*) describing the API.
+First, create a JSON file ( in this example, called *weather_openapi.json*) describing the API.
 
   ```json
   {
-    "openapi": "3.0.0",
+    "openapi": "3.1.0",
     "info": {
-      "title": "Snowfall API",
-      "version": "1.0.0"
+      "title": "get weather data",
+      "description": "Retrieves current weather data for a location based on wttr.in.",
+      "version": "v1.0.0"
     },
+    "servers": [
+      {
+        "url": "https://wttr.in"
+      }
+    ],
+    "auth": [],
     "paths": {
-      "/snow": {
+      "/{location}": {
         "get": {
-          "summary": "Get snowfall information",
+          "description": "Get weather information for a specific location",
+          "operationId": "GetCurrentWeather",
           "parameters": [
             {
               "name": "location",
-              "in": "query",
+              "in": "path",
+              "description": "City or location to retrieve the weather for",
               "required": true,
               "schema": {
                 "type": "string"
               }
+            },
+            {
+            "name": "format",
+            "in": "query",
+            "description": "Always use j1 value for this parameter",
+            "required": true,
+            "schema": {
+              "type": "string",
+              "default": "j1"
             }
+          }
           ],
           "responses": {
             "200": {
               "description": "Successful response",
               "content": {
-                "application/json": {
+                "text/plain": {
                   "schema": {
-                    "type": "object",
-                    "properties": {
-                      "location": {"type": "string"},
-                      "snow": {"type": "string"}
-                    }
+                    "type": "string"
                   }
                 }
               }
+            },
+            "404": {
+              "description": "Location not found"
             }
-          }
+          },
+          "deprecated": false
         }
       }
+    },
+    "components": {
+      "schemes": {}
     }
   }
   ```
 
 Then, register the OpenAPI tool in the agent defintion:
 
   ```python
-  from azure.ai.agents.models import OpenApiTool, OpenApiAnonymousAuthDetails
-  
-  with open("snowfall_openapi.json", "r") as f:
-      openapi_spec = json.load(f)
+  from azure.ai.projects.models import OpenApiTool, OpenApiAnonymousAuthDetails
 
-  auth = OpenApiAnonymousAuthDetails()
-  openapi_tool = OpenApiTool(name="snowfall_api", spec=openapi_spec, auth=auth)
+  with open(weather_asset_file_path, "r") as f:
+        openapi_weather = cast(dict[str, Any], jsonref.loads(f.read()))
+
+  tool = OpenApiTool(
+      openapi=OpenApiFunctionDefinition(
+          name="get_weather",
+          spec=openapi_weather,
+          description="Retrieve weather information for a location.",
+          auth=OpenApiAnonymousAuthDetails(),
+      )
+  )
 
-  agent = agent_client.create_agent(
-      model="gpt-4o-mini",
-      name="openapi-agent",
-      instructions="You are a snowfall tracking assistant. Use the API to fetch snowfall data.",
-      tools=[openapi_tool]
+agent = project_client.agents.create_version(
+      agent_name="openapi-agent",
+      definition=PromptAgentDefinition(
+          model="gpt-4.1",
+          instructions="You are a weather assistant. Use the API to fetch weather data.",
+          tools=[openapi_tool],
+      ),
   )
   ```
 
-The agent can now use the OpenAPI tool to fetch snowfall data dynamically.
+The agent can now use the OpenAPI tool to fetch weather data dynamically.
 
 > [!NOTE]
 > One of the concepts related to agents and custom tools that developers often have difficulty with is the *declarative* nature of the solution. You don't need to write code that explicitly *calls* your custom tool functions - the agent itself decides to call tool functions based on messages in prompts. By providing the agent with functions that have meaningful names and well-documented parameters, the agent can "figure out" when and how to call the function all by itself!

learn-pr/wwl-data-ai/build-agent-with-custom-tools/includes/5-exercise.md

@@ -5,7 +5,7 @@ Now it's your opportunity to build an agent with custom tools. In this exercise,
 
 Launch the exercise and follow the instructions.
 
-[![Button to launch exercise.](../media/launch-exercise.png)](https://go.microsoft.com/fwlink/?linkid=2309805&azure-portal=true)
+[![Button to launch exercise.](../media/launch-exercise.png)](https://go.microsoft.com/fwlink/?linkid=2353623&azure-portal=true)
 
 > [!TIP]
 > After completing the exercise, if you're finished exploring Azure AI Agents, delete the Azure resources that you created during the exercise.

learn-pr/wwl-data-ai/build-agent-with-custom-tools/index.yml

@@ -3,7 +3,7 @@ uid: learn.wwl.build-agent-with-custom-tools
 metadata:
   title: Integrate custom tools into your agent
   description: Learn how to build an agent with custom tools using the Microsoft Foundry Agent Service.
-  ms.date: 08/28/2025
+  ms.date: 02/24/2026
   author: ivorb
   ms.author: berryivor
   ms.topic: module-standard-task-based