Containerizing on William Denniss

Containerizing an ADK agent

Sat, 20 Jun 2026 20:39:04 +0000

So far we’ve run the trading agent two ways: locally with adk run, and deployed to Agent Platform runtime with a little Python script that packages the code and hosts it for us. Both are handy, but in each case something else is deciding how your agent gets hosted. Sometimes you just want a plain container — one image you can run on your laptop today, and on Cloud Run, GKE, or Agent Platform runtime (in bring-your-own-container mode) tomorrow. Same artifact, runs anywhere Docker does.

Agent Container for Agent Platform runtime

Mon, 15 Jun 2026 18:39:04 +0000

Deploy Agent Container to Agent Platform runtime

Mon, 15 Jun 2026 18:39:04 +0000

Deploy an ADK Agent container to Cloud Run

Mon, 15 Jun 2026 18:39:04 +0000

Deploy an ADK Agent container to GKE

Mon, 15 Jun 2026 18:39:04 +0000

Creating an Agent Platform resource identifier

Tue, 09 Jun 2026 13:54:14 -0700

To use Agent Platform services like Memory Bank, and Sessions from other environments, you need to first create a resource that is currently known as an “agent engine”.

Note: if you are familiar with the Python ADK libraries, you can follow these instructions instead, to create an instance via the SDK.

Here’s a single API call to create a resource identifier that you can use right away:

# 1. Set your variables
PROJECT_ID=$(gcloud config get-value project)
LOCATION="us-west1" # Replace with your target region
RESOURCE_DISPLAY_NAME="My Memory Bank"

# 2. Execute the REST API request
curl -X POST \
  -H "Authorization: Bearer $(gcloud auth print-access-token)" \
  -H "Content-Type: application/json" \
  "https://${LOCATION}-aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/${LOCATION}/reasoningEngines" \
  -d "{
    \"displayName\": \"${RESOURCE_DISPLAY_NAME}\"
  }"

The creation returns immediately with an operation id, and executes async, you can query the API with the operation, or look up the Deployments UI to get the resource.