Manage deployed agents

This page describes how to manage agents that have been deploy to the Vertex AI Agent Engine managed runtime. Deployed agents are resources of type reasoningEngine in Vertex AI.

List deployed agents

List all deployed agents for a given project and location:

Console

  1. In the Google Cloud console, go to the Vertex AI Agent Engine page.

    Go to Agent Engine

Deployed agents that are part of the selected project appear in the list. You can use the Filter field to filter the list by your specified column.

Vertex AI SDK for Python 

from vertexai import agent_engines

agent_engines.list()

To filter the list of by display_name:

from vertexai import agent_engines

agent_engines.list(filter='display_name="Demo Langchain Agent"')

REST

Call the reasoningEngines.list method.

Before using any of the request data, make the following replacements:

  • PROJECT_ID: your GCP project ID
  • LOCATION: a supported region

HTTP method and URL: 

GET https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/reasoningEngines

To send your request, expand one of these options:

You should receive a successful status code (2xx) and an empty response.

Get a deployed agent

Each deployed agent has a unique RESOURCE_ID identifier. To learn more, see Deploy an agent.

Console

  1. In the Google Cloud console, go to the Vertex AI Agent Engine page.

    Go to Agent Engine

    Deployed agents that are part of the selected project appear in the list. You can use the Filter field to filter the list by your specified column.

  2. Click the name of the specified agent. The Metrics page for the agent opens.

  3. (Optional) To view deployment details for the agent, click Deployment details. The Deployment details pane opens. To close the pane, click Done.

  4. (Optional) To view the query and streamQuery URLs for the agent, click API URLs. The API URLs pane opens. To close the pane, click Done.

Vertex AI SDK for Python

The following code lets you get a specific deployed agent:

from vertexai import agent_engines

remote_agent = agent_engines.get("RESOURCE_ID")

Alternately, you can provide the fully qualified resource name:

from vertexai import agent_engines

remote_agent = agent_engines.get(
"projects/PROJECT_ID_OR_NUMBER/locations/LOCATION/reasoningEngines/RESOURCE_ID"
)

REST

Call the reasoningEngines.get method.

Before using any of the request data, make the following replacements:

  • PROJECT_ID: your GCP project ID
  • LOCATION: a supported region
  • RESOURCE_ID: the resource ID of the deployed agent

HTTP method and URL: 

GET https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/reasoningEngines/RESOURCE_ID

To send your request, expand one of these options:

You should receive a successful status code (2xx) and an empty response.

Update a deployed agent

You can update one or more fields of the deployed agent at the same time, but you have to specify at least one of the fields to be updated. The amount of time it takes to update the deployed agent depends on the update being performed, but it generally takes between a few seconds to a few minutes.

Console

  1. In the Google Cloud console, go to the Vertex AI Agent Engine page.

    Go to Agent Engine

  2. For your specified agent, click more actions menu ().

  3. Click Edit. The Edit pane for the agent opens.

  4. Edit the Display name or Description for the agent.

  5. Click Save.

Vertex AI SDK for Python

To update a deployed agent (corresponding to RESOURCE_NAME) to an updated agent (corresponding to UPDATED_AGENT):

from vertexai import agent_engines

agent_engines.update(
    resource_name=RESOURCE_NAME,    # Required.
    agent_engine=UPDATED_AGENT,     # Optional.
    requirements=REQUIREMENTS,      # Optional.
    display_name="DISPLAY_NAME",    # Optional.
    description="DESCRIPTION",      # Optional.
    extra_packages=EXTRA_PACKAGES,  # Optional.
)

The arguments are the same as when you are deploying an agent. You can find details in the API reference.

REST

Call the reasoningEngines.patch method and provide an update_mask to specify which fields to update.

Before using any of the request data, make the following replacements:

  • PROJECT_ID: your GCP project ID
  • LOCATION: a supported region
  • RESOURCE_ID: the resource ID of the deployed agent
  • update_mask: a list of comma-separated fields to update

HTTP method and URL: 

PATCH https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/reasoningEngines/RESOURCE_ID?update_mask="display_name,description"

Request JSON body: 

{
"displayName": "DISPLAY_NAME",
"description": "DESCRIPTION"
}

To send your request, expand one of these options:

You should receive a successful status code (2xx) and an empty response.

Delete a deployed agent

Delete a deployed agent from the Vertex AI Agent Engine managed runtime.

Console

  1. In the Google Cloud console, go to the Vertex AI Agent Engine page.

    Go to Agent Engine

  2. For your specified agent, click more actions menu ().

  3. Click Delete.

  4. Click Delete agent.

Vertex AI SDK for Python

If you already have an existing instance of the deployed agent (as remote_agent), you can run the following command:

remote_agent.delete()

Alternatively, you can call agent_engines.delete() to delete the deployed agent corresponding to RESOURCE_NAME in the following way:

from vertexai import agent_engines

agent_engines.delete(RESOURCE_NAME)

REST

Call the reasoningEngines.delete method.

Before using any of the request data, make the following replacements:

  • PROJECT_ID: your GCP project ID
  • LOCATION: a supported region
  • RESOURCE_ID: the resource ID of the deployed agent

HTTP method and URL: 

DELETE https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/reasoningEngines/RESOURCE_ID

To send your request, expand one of these options:

You should receive a successful status code (2xx) and an empty response.

What's next