Agent Framework update to observability docs (#750)

* update to observability docs

* updated note tags

Author: eavanvalkenburg

agent-framework/tutorials/agents/enable-observability.md

@@ -150,12 +150,16 @@ The following OpenTelemetry packages are included by default:
 ```text
 opentelemetry-api
 opentelemetry-sdk
-azure-monitor-opentelemetry
-azure-monitor-opentelemetry-exporter
 opentelemetry-exporter-otlp-proto-grpc
 opentelemetry-semantic-conventions-ai
 ```
 
+If you want to export to Azure Monitor (Application Insights), you also need to install the `azure-monitor-opentelemetry` package:
+
+```bash
+pip install azure-monitor-opentelemetry
+```
+
 ## Enable OpenTelemetry in your app
 
 Agent Frameworkagent framework provides a convenient `setup_observability` function that configures OpenTelemetry with sensible defaults.
@@ -179,12 +183,15 @@ The `setup_observability` function accepts the following parameters to customize
 
 - **`otlp_endpoint`** (str, optional): The OTLP endpoint URL for exporting telemetry data. Default is `None`. Commonly set to `http://localhost:4317`. This creates an OTLPExporter for spans, metrics, and logs. Can be used with any OTLP-compliant endpoint such as [OpenTelemetry Collector](https://opentelemetry.io/docs/collector/), [Aspire Dashboard](/dotnet/aspire/fundamentals/dashboard/overview?tabs=bash), or other OTLP endpoints. Can also be set via `OTLP_ENDPOINT` environment variable.
 
-- **`applicationinsights_connection_string`** (str, optional): Azure Application Insights connection string for exporting to Azure Monitor. Default is `None`. Creates AzureMonitorTraceExporter, AzureMonitorMetricExporter, and AzureMonitorLogExporter. You can find this connection string in the Azure portal under the "Overview" section of your Application Insights resource. Can also be set via `APPLICATIONINSIGHTS_CONNECTION_STRING` environment variable.
+- **`applicationinsights_connection_string`** (str, optional): Azure Application Insights connection string for exporting to Azure Monitor. Default is `None`. Creates AzureMonitorTraceExporter, AzureMonitorMetricExporter, and AzureMonitorLogExporter. You can find this connection string in the Azure portal under the "Overview" section of your Application Insights resource. Can also be set via `APPLICATIONINSIGHTS_CONNECTION_STRING` environment variable. Requires installation of the `azure-monitor-opentelemetry` package.
 
 - **`vs_code_extension_port`** (int, optional): Port number for the AI Toolkit or Azure AI Foundry VS Code extension. Default is `4317`. Allows integration with VS Code extensions for local development and debugging. Can also be set via `VS_CODE_EXTENSION_PORT` environment variable.
 
 - **`exporters`** (list, optional): Custom list of OpenTelemetry exporters for advanced scenarios. Default is `None`. Allows you to provide your own configured exporters when the standard options don't meet your needs.
 
+> [!IMPORTANT]
+> When no exporters (either through parameters or environment variables or as explicit exporters) are provided, the console exporter is configured by default for local debugging.
+
 ### Setup options
 
 You can configure observability in three ways:
@@ -207,6 +214,7 @@ setup_observability()  # Reads from environment variables
 ```python
 from agent_framework.observability import setup_observability
 
+# note that ENABLE_OTEL is implied to be True when calling setup_observability programmatically
 setup_observability(
     enable_sensitive_data=True,
     otlp_endpoint="http://localhost:4317",
@@ -327,22 +335,37 @@ The following metrics are also collected:
 
 ## Azure AI Foundry integration
 
-If you're using Azure AI Foundry, there's a convenient method for automatic setup:
+If you're using Azure AI Foundry clients, there's a convenient method for automatic setup:
 
 ```python
 from agent_framework.azure import AzureAIAgentClient
 from azure.identity import AzureCliCredential
 
 agent_client = AzureAIAgentClient(
     credential=AzureCliCredential(),
-    project_endpoint="https://<your-project>.foundry.azure.com"
+    # endpoint and model_deployment_name can be taken from environment variables
+    # project_endpoint="https://<your-project>.foundry.azure.com"
+    # model_deployment_name="<your-deployment-name>"
 )
 
 # Automatically configures observability with Application Insights
 await agent_client.setup_azure_ai_observability()
 ```
 
-This method retrieves the Application Insights connection string from your Azure AI Foundry project and calls `setup_observability` automatically.
+This method retrieves the Application Insights connection string from your Azure AI Foundry project and calls `setup_observability` automatically. If you want to use Foundry Telemetry with other types of agents, you can do the same thing with:
+```python
+from agent_framework.observability import setup_observability
+from azure.ai.projects import AIProjectClient
+from azure.identity import AzureCliCredential
+
+project_client = AIProjectClient(endpoint, credential=AzureCliCredential())
+conn_string = project_client.telemetry.get_application_insights_connection_string()
+setup_observability(applicationinsights_connection_string=conn_string)
+```
+Also see the [relevant Foundry documentation](/azure/ai-foundry/how-to/develop/trace-agents-sdk).
+
+> [!NOTE]
+> When using Azure Monitor for your telemetry, you need to install the `azure-monitor-opentelemetry` package explicitly, as it is not included by default with Agent Framework.
 
 ## Next steps
 
@@ -352,4 +375,3 @@ For more advanced observability scenarios and examples, see the [Agent Observabi
 > [Persisting conversations](./persisted-conversation.md)
 
 ::: zone-end
-