Deploy an agent

To deploy an agent on Vertex AI Agent Engine, use the following steps:

1. Configure your agent for deployment.
2. Create an AgentEngine instance.
3. Grant the deployed agent permissions.
4. Get the agent resource ID.

You can also use Agent Starter Pack templates for deployment.

Before you begin

Before you deploy an agent, make sure you have completed the following tasks:

1. Set up your environment.
2. Develop an agent.

Configure your agent for deployment

You can make the following optional configurations:

• Package requirements
• Additional packages
• Environment variables
• Build options
• Cloud Storage folder
• Resource metadata
• Custom service account

Define the package requirements

Provide the set of packages required by the agent for deployment. The set of packages can either be a list of items to be installed by pip, or the path to a file that follows the Requirements File Format. Use the following best practices:

1. Pin your package versions for reproducible builds. Common packages to keep track of include the following: google-cloud-aiplatform, cloudpickle, langchain, langchain-core, langchain-google-vertexai, and pydantic.

2. Minimize the number of dependencies in your agent. This reduces the number of breaking changes when updating your dependencies and agent.

If the agent doesn't have any dependencies, you can set requirements to None:

requirements = None

If the agent uses a framework-specific template, you should specify the SDK version that is imported (such as 1.77.0) when developing the agent.

requirements = [
    "google-cloud-aiplatform[agent_engines,adk]",
    # any other dependencies
]

requirements = [
    "google-cloud-aiplatform[agent_engines,langchain]",
    # any other dependencies
]

requirements = [
    "google-cloud-aiplatform[agent_engines,langgraph]",
    # any other dependencies
]

requirements = [
    "google-cloud-aiplatform[agent_engines,ag2]",
    # any other dependencies
]

requirements = [
    "google-cloud-aiplatform[agent_engines,llama_index]",
    # any other dependencies
]

You can also do the following with package requirements:

• Upper-bound or pin the version of a given package (such as google-cloud-aiplatform):

  requirements = [
      # See https://pypi.org/project/google-cloud-aiplatform for the latest version.
      "google-cloud-aiplatform[agent_engines,adk]==1.88.0",
  ]
  

• Add additional packages and constraints:

  requirements = [
      "google-cloud-aiplatform[agent_engines,adk]==1.88.0",
      "cloudpickle==3.0", # new
  ]
  

• Point to the version of a package on a GitHub branch or pull request:

  requirements = [
      "google-cloud-aiplatform[agent_engines,adk] @ git+https://github.com/googleapis/python-aiplatform.git@BRANCH_NAME", # new
      "cloudpickle==3.0",
  ]
  

• Maintain the list of requirements in a file (such as path/to/requirements.txt):

  requirements = "path/to/requirements.txt"
  

  where path/to/requirements.txt is a text file that follows the Requirements File Format. For example:

  google-cloud-aiplatform[agent_engines,adk]
  cloudpickle==3.0
  

Define additional packages

You can include local files or directories that contain local required Python source files. Compared to package requirements, this lets you use private utilities you have developed that aren't otherwise available on PyPI or GitHub.

If the agent does not require any extra packages, you can set extra_packages to None:

extra_packages = None

You can also do the following with extra_packages:

• Include a single file (such as agents/agent.py):

  extra_packages = ["agents/agent.py"]
  

• Include the set of files in an entire directory (for example, agents/):

  extra_packages = ["agents"] # directory that includes agents/agent.py
  

• Specify Python wheel binaries (for example, path/to/python_package.whl):

  requirements = [
      "google-cloud-aiplatform[agent_engines,adk]",
      "cloudpickle==3.0",
      "python_package.whl",  # install from the whl file that was uploaded
  ]
  extra_packages = ["path/to/python_package.whl"]  # bundle the whl file for uploading
  

Define environment variables

If there are environment variables that your agent depends on, you can specify them in the env_vars= argument. If the agent does not depend on any environment variables, you can set it to None:

env_vars = None

To specify the environment variables, there are a few different options available:

env_vars = {
  "VARIABLE_1": "VALUE_1",
  "VARIABLE_2": "VALUE_2",
}
# These environment variables will become available in Vertex AI Agent Engine
# through `os.environ`, e.g.
#
#   import os
#   os.environ["VARIABLE_1"] # will have the value "VALUE_1"
#
# and
#
#   os.environ["VARIABLE_2"] # will have the value "VALUE_2"
#

env_vars = {
  # ... (other environment variables and their values)
  "CLOUD_SQL_CREDENTIALS_SECRET": {"secret": "SECRET_ID", "version": "SECRET_VERSION_ID"},
}

secret = os.environ.get("CLOUD_SQL_CREDENTIALS_SECRET")
if secret:
  # Secrets are stored as strings, so use json.loads to parse JSON payloads.
  return json.loads(secret)

env_vars = ["VARIABLE_1", "VARIABLE_2"]
# This corresponds to the following code snippet:
#
#   import os
#
#   env_vars = {
#     "VARIABLE_1": os.environ["VARIABLE_1"],
#     "VARIABLE_2": os.environ["VARIABLE_2"],
#   }

Define build options

You can specify build options for the agent, such as installation scripts to run when building the agent's container image. This is useful for installing system dependencies (for example, gcloud cli, npx) or other custom setups. The scripts are run with root permissions.

To use installation scripts, create a directory named installation_scripts and place your shell scripts inside the directory:

.
├── ...
└── installation_scripts/
    └── install.sh

Next, specify the installation_scripts directory in extra_packages and the script paths in build_options:

extra_packages = [..., "installation_scripts/install.sh"]
build_options = {"installation_scripts": ["installation_scripts/install.sh"]}

Define a Cloud Storage folder

Staging artifacts are overwritten if they correspond to an existing folder in a Cloud Storage bucket. If necessary, you can specify the Cloud Storage folder for the staging artifacts. You can set gcs_dir_name to None if you don't mind potentially overwriting the files in the default folder:

gcs_dir_name = None

To avoid overwriting the files (such as for different environments such as development, staging, and production), you can set up corresponding folder, and specify the folder to stage the artifact under:

gcs_dir_name = "dev" # or "staging" or "prod"

If you want or need to avoid collisions, you can generate a random uuid:

import uuid
gcs_dir_name = str(uuid.uuid4())

Configure resource metadata

You can set metadata on the ReasoningEngine resource:

display_name = "Currency Exchange Rate Agent (Staging)"

description = """
An agent that has access to tools for looking up the exchange rate.

If you run into any issues, please contact the dev team.
"""

For a full set of the parameters, see the API reference.

Configure a custom service account

You can configure a custom service account as the identity of your deployed agent, instead of the default identity.

To do so, specify the email of your custom service account as the service_account when creating or updating the Agent Engine instance, for example:

# Create a new instance
agent_engines.create(
  local_agent=<my-agent>,
  service_account="my-custom-service-account@my-project.iam.gserviceaccount.com",
  ...
)

# Update an existing instance
resource_name = "projects/{project_id}/locations/{location}/reasoningEngines/{reasoning_engine_id}"
agent_engines.update(
  resource_name,
  service_account="my-new-custom-service-account@my-project.iam.gserviceaccount.com",
  ...
)

Create an AgentEngine instance

To deploy the agent on Vertex AI, use agent_engines.create to pass in the local_agent object along with any optional configurations:

remote_agent = agent_engines.create(
    local_agent,                     # Optional.
    requirements=requirements,       # Optional.
    extra_packages=extra_packages,   # Optional.
    gcs_dir_name=gcs_dir_name,       # Optional.
    display_name=display_name,       # Optional.
    description=description,         # Optional.
    env_vars=env_vars,               # Optional.
    build_options=build_options,     # Optional.
    service_account=service_account, # Optional.
)

Deployment takes a few minutes, during which the following steps happen in the background:

1. A bundle of the following artifacts are generated locally:

   • *.pkl a pickle file corresponding to local_agent.
   • requirements.txt a text file containing the package requirements.
   • dependencies.tar.gz a tar file containing any extra packages.

2. The bundle is uploaded to Cloud Storage (under the corresponding folder) for staging the artifacts.

3. The Cloud Storage URIs for the respective artifacts are specified in the PackageSpec.

4. The Vertex AI Agent Engine service receives the request and builds containers and starts HTTP servers on the backend.

Deployment latency depends on the total time it takes to install required packages. Once deployed, remote_agent corresponds to an instance of local_agent that is running on Vertex AI and can be queried or deleted. It is separate from local instances of the agent.

Grant the deployed agent permissions

If the deployed agent needs to be granted any additional permissions, follow the instructions in Set up the identity and permissions for your agent.

If you defined secrets as environment variables, you need to grant the following permission:

• Secret Manager Secret Accessor (roles/secretmanager.secretAccessor)

Get the agent resource ID

Each deployed agent has a unique identifier. You can run the following command to get the resource_name identifier for your deployed agent:

remote_agent.resource_name

The response should look like the following string:

"projects/PROJECT_NUMBER/locations/LOCATION/reasoningEngines/RESOURCE_ID"

where

• PROJECT_ID is the Google Cloud project ID where the deployed agent runs.

• LOCATION is the region where the deployed agent runs.

• RESOURCE_ID is the ID of the deployed agent as a reasoningEngine resource.

What's next

• Use the agent.
• Manage deployed agents.
• Troubleshoot deploying an agent.
• Get support.