Agent Platform 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

Deterministic memories

Mon, 15 Jun 2026 18:37:26 +0000

The Trading agent we deployed earlier will automatically have session persistence when deployed on Agent Platform. You can test this by asking it a day later “What trades did you execute” from the same session that you executed the trades from. The full conversation history is preserved, even while the agent is suspended (you’re not paying for it to run continuously).

You can also inspect the saved session data from the console.

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.

Agent memories with ADK

Mon, 01 Jun 2026 16:16:44 -0700

ADK Agents will automatically have session persistence when deployed on Agent Platform. You can test this by asking follow up questions in the running sessions like “What trades did you execute?”. The full conversation history is preserved, even while the agent is suspended (you’re not paying for it to run continuously), and is visibile in the console.

When you start a new session however, that context is gone. It’s common to want some level of memory and personalization for an agent. For example, if the user says “I never want to buy ___ stock”, ideally we would remember this and factor that in to trading decisions.

Deploy an ADK agent to Agent Platform Runtime

Fri, 15 May 2026 11:03:02 -0700

In my previous post, we created an agent with ADK and ran it locally. The next step is to deploy it to Agent Platform runtime.

You can deploy to the runtime using a Python script that imports your agent, and configures the remote resource, and performs the deployment. This approach serializes your live object graph which is simple, but not suitable for production. We’ll cover production hardening later.

Create the following script in the root project folder (the one containing trading_agent from the previous example). There’s nothing to edit in it: the project ID is read from your environment (via Application Default Credentials), and the staging bucket name is derived from it (gs://${PROJECT_ID}-trading-agent), with the bucket created on first run if it doesn’t exist. Just point gcloud at the right project first:

Building a stock-trading agent with ADK

Wed, 22 Apr 2026 11:17:03 -0700

The Agent Development Kit (ADK) is Google’s open-source Python framework for building AI agents. You write the agent’s logic and the tools it can call, and ADK handles orchestrating everything — with evaluation and deployment built in too.

I wanted a slightly more interesting app than Hello World to exercise more parts of the platform, so I figured I’d write a little stock trading agent that applies a sentiment momentum trading strategy. We’ll use ADK and the Gemini API to execute trades on Alpaca, chosen for its REST API and free paper-trading account.