Fix missing await in telemetry wrapper methods for async Agent calls (#43606)

Author: dargilco

sdk/ai/azure-ai-agents/CHANGELOG.md

@@ -2,6 +2,13 @@
 
 # Release History
 
+## 1.2.0b6 (2025-10-24)
+
+### Bugs Fixed
+
+- Fix missing `await` in calls to asynchronous Agent methods, when `AIAgentsInstrumentor().instrument()`
+is called but no tracer is configured
+
 ## 1.2.0b5 (2025-09-29)
 
 ### Features Added

sdk/ai/azure-ai-agents/azure/ai/agents/_version.py

@@ -6,4 +6,4 @@
 # Changes may cause incorrect behavior and will be lost if the code is regenerated.
 # --------------------------------------------------------------------------
 
-VERSION = "1.2.0b5"
+VERSION = "1.2.0b6"

sdk/ai/azure-ai-agents/azure/ai/agents/telemetry/_ai_agents_instrumentor.py

@@ -1617,7 +1617,7 @@ def _trace_async_function(
         async def inner(*args, **kwargs):  # pylint: disable=R0911
             span_impl_type = settings.tracing_implementation()  # pylint: disable=E1102
             if span_impl_type is None:
-                return function(*args, **kwargs)
+                return await function(*args, **kwargs)
 
             class_function_name = function.__qualname__
 

sdk/ai/azure-ai-agents/samples/agents_async/sample_agents_basics_with_console_tracing_async.py

@@ -0,0 +1,100 @@
+# ------------------------------------
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+# ------------------------------------
+
+"""
+DESCRIPTION:
+    This sample demonstrates how to use basic agent operations from
+    the Azure Agents service using an asynchronous client with tracing to console.
+
+USAGE:
+    python sample_agents_basics_with_console_tracing_async.py
+
+    Before running the sample:
+
+    pip install azure-ai-projects azure-ai-agents azure-identity opentelemetry-sdk azure-core-tracing-opentelemetry aiohttp
+
+    If you want to export telemetry to OTLP endpoint (such as Aspire dashboard
+    https://learn.microsoft.com/dotnet/aspire/fundamentals/dashboard/standalone?tabs=bash)
+    install:
+
+    pip install azure-ai-projects opentelemetry-exporter-otlp-proto-grpc
+
+    Set these environment variables with your own values:
+    1) PROJECT_ENDPOINT - The Azure AI Project endpoint, as found in the Overview
+                          page of your Azure AI Foundry portal.
+    2) MODEL_DEPLOYMENT_NAME - The deployment name of the AI model, as found under the "Name" column in
+       the "Models + endpoints" tab in your Azure AI Foundry project.
+    3) OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT - Optional. Set to `true` to trace the content of chat
+       messages, which may contain personal data. False by default.
+"""
+
+import asyncio
+import os
+from azure.ai.projects.aio import AIProjectClient
+from azure.ai.agents.models import ListSortOrder
+from azure.identity.aio import DefaultAzureCredential
+
+from azure.core.settings import settings
+settings.tracing_implementation = "opentelemetry"
+
+# Install opentelemetry with command "pip install azure-ai-projects opentelemetry-sdk".
+from opentelemetry import trace
+from opentelemetry.sdk.trace import TracerProvider
+from opentelemetry.sdk.trace.export import SimpleSpanProcessor, ConsoleSpanExporter
+
+# Setup tracing to console
+# Requires opentelemetry-sdk
+span_exporter = ConsoleSpanExporter()
+tracer_provider = TracerProvider()
+tracer_provider.add_span_processor(SimpleSpanProcessor(span_exporter))
+trace.set_tracer_provider(tracer_provider)
+tracer = trace.get_tracer(__name__)
+
+from azure.ai.agents.telemetry import AIAgentsInstrumentor
+AIAgentsInstrumentor().instrument()
+
+
+async def main() -> None:
+
+    credential = DefaultAzureCredential()
+
+    async with credential:
+
+        project_client = AIProjectClient(
+            endpoint=os.environ["PROJECT_ENDPOINT"],
+            credential=credential,
+        )
+
+        scenario = os.path.basename(__file__)
+        with tracer.start_as_current_span(scenario):
+
+            async with project_client:
+
+                agent = await project_client.agents.create_agent(
+                    model=os.environ["MODEL_DEPLOYMENT_NAME"], name="my-agent", instructions="You are helpful agent"
+                )
+                print(f"Created agent, agent ID: {agent.id}")
+
+                thread = await project_client.agents.threads.create()
+                print(f"Created thread, thread ID: {thread.id}")
+
+                message = await project_client.agents.messages.create(thread_id=thread.id, role="user", content="Hello, tell me a joke")
+                print(f"Created message, message ID: {message.id}")
+
+                run = await project_client.agents.runs.create_and_process(thread_id=thread.id, agent_id=agent.id)
+                print(f"Run completed with status: {run.status}")
+
+                await project_client.agents.delete_agent(agent.id)
+                print("Deleted agent")
+
+                messages = project_client.agents.messages.list(thread_id=thread.id, order=ListSortOrder.ASCENDING)
+                async for msg in messages:
+                    if msg.text_messages:
+                        last_text = msg.text_messages[-1]
+                        print(f"{msg.role}: {last_text.text.value}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())