work iq samples (#47184)

* work iq samples

* Add work IQ project connection and user input to service preparer

* update tests

* push assets.json

---------

Co-authored-by: Jessie Li <jessli@microsoft.com>

Author: howieleung

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

@@ -71,6 +71,10 @@ Breaking changes in beta classes:
   * `sample_models_basic.py` — synchronous end-to-end registration via the `create` helper (uses `azcopy`), followed by `get`, `list_versions`, `list`, `get_credentials`, `update`, and `delete`.
   * `sample_models_create_and_poll.py` — alternative synchronous registration that hand-rolls the spec's three-step flow (`pending_upload` → upload via `azure-storage-blob` → `pending_create_version` + poll), without taking a dependency on `azcopy`.
   * `sample_models_basic_async.py` — asynchronous version of the same three-step flow using `azure.ai.projects.aio.AIProjectClient` and `azure.storage.blob.aio.ContainerClient`.
+* Added new evaluation sample `sample_model_evaluation_instant_model.py` demonstrating model evaluation with an instant model.
+* Added new Agent tool samples `sample_agent_work_iq.py` and `sample_agent_work_iq_async.py` demonstrating use of `WorkIQPreviewTool`.
+* Added new Agent tool samples `sample_agent_fabric_iq.py` and `sample_agent_fabric_iq_async.py` demonstrating use of `FabricIQPreviewTool`.
+* Refreshed evaluation samples under `samples/evaluations/` and `samples/evaluations/agentic_evaluators/` (including `sample_agent_evaluation`, `sample_agent_response_evaluation`, `sample_eval_catalog_prompt_based_evaluators`, `sample_evaluations_ai_assisted`, `sample_evaluations_builtin_with_csv`, `sample_evaluations_builtin_with_dataset_id`, `sample_evaluations_builtin_with_inline_data`, `sample_evaluations_builtin_with_inline_data_oai`, `sample_scheduled_evaluations`, `sample_coherence`, `sample_fluency`, `sample_intent_resolution`, `sample_relevance`, `sample_response_completeness`, `sample_tool_call_accuracy`, `sample_tool_call_success`, `sample_tool_input_accuracy`, `sample_tool_output_utilization`, `sample_tool_selection`, and `sample_generic_agentic_evaluator`).
 * New sample `sample_dataset_generation_job_simpleqna_with_prompt_source.py` showing an end-to-end flow that generates a QnA dataset via `.beta.datasets.create_generation_job` and runs an OpenAI evaluation.
 
 ## 2.1.0 (2026-04-20)

sdk/ai/azure-ai-projects/assets.json

@@ -2,5 +2,5 @@
   "AssetsRepo": "Azure/azure-sdk-assets",
   "AssetsRepoPrefixPath": "python",
   "TagPrefix": "python/ai/azure-ai-projects",
-  "Tag": "python/ai/azure-ai-projects_7fc443e02a"
+  "Tag": "python/ai/azure-ai-projects_7c0b4d3cf0"
 }

sdk/ai/azure-ai-projects/samples/agents/tools/sample_agent_work_iq.py

@@ -0,0 +1,68 @@
+# pylint: disable=line-too-long,useless-suppression
+# ------------------------------------
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+# ------------------------------------
+
+"""
+DESCRIPTION:
+    This sample demonstrates how to run a Prompt Agent that uses the
+    WorkIQ preview tool with a synchronous client.
+
+USAGE:
+    python sample_agent_work_iq.py
+
+    Before running the sample:
+
+    pip install "azure-ai-projects>=2.2.0" python-dotenv
+
+    Set these environment variables with your own values:
+    1) FOUNDRY_PROJECT_ENDPOINT - The Azure AI Project endpoint, as found in the Overview
+       page of your Microsoft Foundry portal.
+    2) FOUNDRY_MODEL_NAME - The deployment name of the AI model, as found under the "Name" column in
+       the "Models + endpoints" tab in your Microsoft Foundry project.
+    3) WORK_IQ_PROJECT_CONNECTION_ID - The fully-qualified resource id of the WorkIQ project connection.
+    4) WORK_IQ_USER_INPUT - The natural-language question to send to the agent.
+"""
+
+import os
+from dotenv import load_dotenv
+from azure.identity import DefaultAzureCredential
+from azure.ai.projects import AIProjectClient
+from azure.ai.projects.models import PromptAgentDefinition, WorkIQPreviewTool
+
+load_dotenv()
+
+endpoint = os.environ["FOUNDRY_PROJECT_ENDPOINT"]
+
+with (
+    DefaultAzureCredential() as credential,
+    AIProjectClient(endpoint=endpoint, credential=credential) as project_client,
+    project_client.get_openai_client() as openai_client,
+):
+    tool_payload = WorkIQPreviewTool(
+        project_connection_id=os.environ["WORK_IQ_PROJECT_CONNECTION_ID"],
+    )
+
+    agent = project_client.agents.create_version(
+        agent_name="MyAgent",
+        definition=PromptAgentDefinition(
+            model=os.environ["FOUNDRY_MODEL_NAME"],
+            instructions="Use the available WorkIQ tools to answer questions and perform tasks.",
+            tools=[tool_payload],
+        ),
+    )
+    print(f"Agent created (id: {agent.id}, name: {agent.name}, version: {agent.version})")
+
+    user_input = os.environ.get("WORK_IQ_USER_INPUT") or input("Enter your question:\n")
+
+    response = openai_client.responses.create(
+        input=user_input,
+        extra_body={"agent_reference": {"name": agent.name, "type": "agent_reference"}},
+    )
+
+    print(f"Agent response: {response.output_text}")
+
+    # Clean up the agent version so unused versions don't accumulate in the project.
+    project_client.agents.delete_version(agent_name=agent.name, agent_version=agent.version)
+    print("Agent deleted")

sdk/ai/azure-ai-projects/samples/agents/tools/sample_agent_work_iq_async.py

@@ -0,0 +1,75 @@
+# pylint: disable=line-too-long,useless-suppression
+# ------------------------------------
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+# ------------------------------------
+
+"""
+DESCRIPTION:
+    This sample demonstrates how to run a Prompt Agent that uses the
+    WorkIQ preview tool with an asynchronous client.
+
+USAGE:
+    python sample_agent_work_iq_async.py
+
+    Before running the sample:
+
+    pip install "azure-ai-projects>=2.2.0" python-dotenv aiohttp
+
+    Set these environment variables with your own values:
+    1) FOUNDRY_PROJECT_ENDPOINT - The Azure AI Project endpoint, as found in the Overview
+       page of your Microsoft Foundry portal.
+    2) FOUNDRY_MODEL_NAME - The deployment name of the AI model, as found under the "Name" column in
+       the "Models + endpoints" tab in your Microsoft Foundry project.
+    3) WORK_IQ_PROJECT_CONNECTION_ID - The fully-qualified resource id of the WorkIQ project connection.
+    4) WORK_IQ_USER_INPUT - The natural-language question to send to the agent.
+"""
+
+import os
+import asyncio
+from dotenv import load_dotenv
+from azure.identity.aio import DefaultAzureCredential
+from azure.ai.projects.aio import AIProjectClient
+from azure.ai.projects.models import PromptAgentDefinition, WorkIQPreviewTool
+
+load_dotenv()
+
+endpoint = os.environ["FOUNDRY_PROJECT_ENDPOINT"]
+
+
+async def main():
+    async with (
+        DefaultAzureCredential() as credential,
+        AIProjectClient(endpoint=endpoint, credential=credential) as project_client,
+        project_client.get_openai_client() as openai_client,
+    ):
+        tool_payload = WorkIQPreviewTool(
+            project_connection_id=os.environ["WORK_IQ_PROJECT_CONNECTION_ID"],
+        )
+
+        agent = await project_client.agents.create_version(
+            agent_name="MyAgent",
+            definition=PromptAgentDefinition(
+                model=os.environ["FOUNDRY_MODEL_NAME"],
+                instructions="Use the available WorkIQ tools to answer questions and perform tasks.",
+                tools=[tool_payload],
+            ),
+        )
+        print(f"Agent created (id: {agent.id}, name: {agent.name}, version: {agent.version})")
+
+        user_input = os.environ.get("WORK_IQ_USER_INPUT") or input("Enter your question:\n")
+
+        response = await openai_client.responses.create(
+            input=user_input,
+            extra_body={"agent_reference": {"name": agent.name, "type": "agent_reference"}},
+        )
+
+        print(f"Agent response: {response.output_text}")
+
+        # Clean up the agent version so unused versions don't accumulate in the project.
+        await project_client.agents.delete_version(agent_name=agent.name, agent_version=agent.version)
+        print("Agent deleted")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

sdk/ai/azure-ai-projects/samples/evaluations/agentic_evaluators/sample_coherence.py

@@ -68,7 +68,7 @@ def main() -> None:
                 type="azure_ai_evaluator",
                 name="coherence",
                 evaluator_name="builtin.coherence",
-                initialization_parameters={"deployment_name": f"{model_deployment_name}"},
+                initialization_parameters={"model": f"{model_deployment_name}"},
                 data_mapping={"query": "{{item.query}}", "response": "{{item.response}}"},
             )
         ]

sdk/ai/azure-ai-projects/samples/evaluations/agentic_evaluators/sample_fluency.py

@@ -68,7 +68,7 @@ def main() -> None:
                 type="azure_ai_evaluator",
                 name="fluency",
                 evaluator_name="builtin.fluency",
-                initialization_parameters={"deployment_name": f"{model_deployment_name}"},
+                initialization_parameters={"model": f"{model_deployment_name}"},
                 data_mapping={"query": "{{item.query}}", "response": "{{item.response}}"},
             )
         ]

sdk/ai/azure-ai-projects/samples/evaluations/agentic_evaluators/sample_generic_agentic_evaluator/sample_generic_agentic_evaluator.py

@@ -37,7 +37,7 @@ def _get_evaluator_initialization_parameters(evaluator_name: str) -> dict[str, s
     if evaluator_name == "task_navigation_efficiency":
         return {"matching_mode": "exact_match"}  #  Can be "exact_match", "in_order_match", or "any_order_match"
     model_deployment_name = os.environ.get("FOUNDRY_MODEL_NAME", "")  # Sample : gpt-4o-mini
-    return {"deployment_name": model_deployment_name}
+    return {"model": model_deployment_name}
 
 
 def _get_evaluation_contents() -> list[SourceFileContentContent]:

sdk/ai/azure-ai-projects/samples/evaluations/agentic_evaluators/sample_intent_resolution.py

@@ -73,7 +73,7 @@ def main() -> None:
                 type="azure_ai_evaluator",
                 name="intent_resolution",
                 evaluator_name="builtin.intent_resolution",
-                initialization_parameters={"deployment_name": f"{model_deployment_name}"},
+                initialization_parameters={"model": f"{model_deployment_name}"},
                 data_mapping={
                     "query": "{{item.query}}",
                     "response": "{{item.response}}",

sdk/ai/azure-ai-projects/samples/evaluations/agentic_evaluators/sample_relevance.py

@@ -72,7 +72,7 @@ def main() -> None:
                 type="azure_ai_evaluator",
                 name="relevance",
                 evaluator_name="builtin.relevance",
-                initialization_parameters={"deployment_name": f"{model_deployment_name}"},
+                initialization_parameters={"model": f"{model_deployment_name}"},
                 data_mapping={"query": "{{item.query}}", "response": "{{item.response}}"},
             )
         ]

sdk/ai/azure-ai-projects/samples/evaluations/agentic_evaluators/sample_response_completeness.py

@@ -70,7 +70,7 @@ def main() -> None:
                 type="azure_ai_evaluator",
                 name="response_completeness",
                 evaluator_name="builtin.response_completeness",
-                initialization_parameters={"deployment_name": f"{model_deployment_name}"},
+                initialization_parameters={"model": f"{model_deployment_name}"},
                 data_mapping={"ground_truth": "{{item.ground_truth}}", "response": "{{item.response}}"},
             )
         ]