First

Author: dargilco

articles/ai-services/agents/how-to/tools/azure-functions.md

@@ -309,7 +309,7 @@ message = project_client.agents.create_message(
 print(f"Created message, message ID: {message.id}")
 
 # Run the agent
-run = project_client.agents.create_run(thread_id=thread.id, assistant_id=agent.id)
+run = project_client.agents.create_run(thread_id=thread.id, agent_id=agent.id)
 # Monitor and process the run status. The function call should be placed on the input queue by the Agent Service for the Azure Function to pick up when requires_action is returned
 while run.status in ["queued", "in_progress", "requires_action"]:
     time.sleep(1)

articles/ai-services/agents/how-to/tools/bing-grounding.md

@@ -340,7 +340,7 @@ Create a run and observe that the model uses the Grounding with Bing Search tool
 
 ```python
 # Create and process agent run in thread with tools
-run = project_client.agents.create_and_process_run(thread_id=thread.id, assistant_id=agent.id)
+run = project_client.agents.create_and_process_run(thread_id=thread.id, agent_id=agent.id)
 print(f"Run finished with status: {run.status}")
 
 # Retrieve run step details to get Bing Search query link

articles/ai-services/agents/how-to/tools/code-interpreter.md

@@ -260,7 +260,7 @@ message = project_client.agents.create_message(
 print(f"Created message, message ID: {message.id}")
 
 # create and execute a run
-run = project_client.agents.create_and_process_run(thread_id=thread.id, assistant_id=agent.id)
+run = project_client.agents.create_and_process_run(thread_id=thread.id, agent_id=agent.id)
 print(f"Run finished with status: {run.status}")
 
 if run.status == "failed":

articles/ai-services/agents/how-to/tools/function-calling.md

@@ -369,7 +369,7 @@ curl $AZURE_AI_AGENTS_ENDPOINT/threads/thread_abc123/messages?api-version=2024-1
 
 ```python
 # Create and process agent run in thread with tools
-run = project_client.agents.create_and_process_run(thread_id=thread.id, assistant_id=agent.id)
+run = project_client.agents.create_and_process_run(thread_id=thread.id, agent_id=agent.id)
 print(f"Run finished with status: {run.status}")
 
 if run.status == "failed":

articles/ai-services/agents/how-to/tools/openapi-spec.md

@@ -320,7 +320,7 @@ Create a run and observe that the model uses the OpenAPI Spec tool to provide a
     print(f"Created message, ID: {message.id}")
 
     # Create and process agent run in thread with tools
-    run = project_client.agents.create_and_process_run(thread_id=thread.id, assistant_id=agent.id)
+    run = project_client.agents.create_and_process_run(thread_id=thread.id, agent_id=agent.id)
     print(f"Run finished with status: {run.status}")
 
     if run.status == "failed":

articles/ai-services/agents/how-to/virtual-networks.md

@@ -246,7 +246,7 @@ with project_client:
     print(f"Created message, message ID: {message.id}")
 
     # Run the agent
-    run = project_client.agents.create_and_process_run(thread_id=thread.id, assistant_id=agent.id)
+    run = project_client.agents.create_and_process_run(thread_id=thread.id, agent_id=agent.id)
     print(f"Run finished with status: {run.status}")
 
     if run.status == "failed":

articles/ai-services/agents/includes/azure-search/code-examples.md

@@ -261,7 +261,7 @@ message = project_client.agents.create_message(
 print(f"Created message, message ID: {message.id}")
 
 # Run the agent
-run = project_client.agents.create_and_process_run(thread_id=thread.id, assistant_id=agent.id)
+run = project_client.agents.create_and_process_run(thread_id=thread.id, agent_id=agent.id)
 print(f"Run finished with status: {run.status}")
 
 if run.status == "failed":

articles/ai-services/agents/includes/file-search/azure-blob-storage-code-examples.md

@@ -78,7 +78,7 @@ message = project_client.agents.create_message(
 )
 print(f"Created message, message ID: {message.id}")
 
-run = project_client.agents.create_and_process_run(thread_id=thread.id, assistant_id=agent_1.id)
+run = project_client.agents.create_and_process_run(thread_id=thread.id, agent_id=agent_1.id)
 
 project_client.agents.delete_vector_store(vector_store.id)
 print("Deleted vector store")

articles/ai-services/agents/includes/file-search/deep-dive.md

@@ -52,7 +52,7 @@ file_search_tool.remove_vector_store(vector_store.id)
 print(f"Removed vector store from file search, vector store ID: {vector_store.id}")
 
 project_client.agents.update_agent(
-    assistant_id=agent.id, tools=file_search_tool.definitions, tool_resources=file_search_tool.resources
+    agent_id=agent.id, tools=file_search_tool.definitions, tool_resources=file_search_tool.resources
 )
 print(f"Updated agent, agent ID: {agent.id}")
 

articles/ai-services/agents/includes/file-search/upload-files-code-examples.md

@@ -310,7 +310,7 @@ curl $AZURE_AI_AGENTS_ENDPOINT/threads/thread_abc123/messages?api-version=2024-1
 Create a run and observe that the model uses the file search tool to provide a response to the user's question.
 # [Python](#tab/python)
 ```python
-run = project_client.agents.create_and_process_run(thread_id=thread.id, assistant_id=agent.id)
+run = project_client.agents.create_and_process_run(thread_id=thread.id, agent_id=agent.id)
 print(f"Created run, run ID: {run.id}")
 
 project_client.agents.delete_vector_store(vector_store.id)