Comment: After iterating on the API Contract post, I asked Codex 5.6 Sol Ultra to form an opinion about when to use Agent Runtime and when to use Cloud Run in the context of a GE app connected agent, and then write a post. I specifically instructed it to avoid the cloudpickle … Continue reading Deploying an ADK agent for Gemini Enterprise: Agent Runtime or Cloud Run?
Agent Substrate can run stateful Google ADK agents across a shared pool of GKE worker Pods. After an agent responds, the harness can suspend its whole process—including its conversation, in-memory working state, and files—and Substrate can restore it on whichever compatible … Continue reading Running stateful agents on Agent Substrate
Agent Runtime can host a container with arbitrary HTTP endpoints. To use it through the Agent Platform Python SDK or the Google Cloud console playground — or through Gemini Enterprise’s native ADK integration — the container must also implement the operations those integrations … Continue reading The Agent Platform API contract: what your agent must expose, and why
With Agent Platform’s bring your own container (BYOC) deployment option, you need to serve the standard API contract yourself. In The Agent Platform API contract: what your agent must expose, and why, I covered why the contract exists and walked through the thirteen methods that … Continue reading Serving the Agent Platform API contract
So you’ve built an ADK agent—in my case the Trading Agent from my post series, an agent that reads S&P 500 news, scores the sentiment, and places paper trades on Alpaca—and now you want it running on Agent Platform’s managed runtime instead of on your laptop. It turns out there … Continue reading 5 ways to deploy agents on Agent Platform
In the previous post we sent the agent’s session events to Memory Bank with add_events_to_memory() in an after-agent callback. An LLM extracts useful facts from those events, and embeddings make the saved facts searchable in a later session. That gives us general recall, but the … Continue reading Deterministic memories
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 … Continue reading Creating an Agent Platform resource identifier
Scroll horizontally to explore. Open full-size diagram 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 … Continue reading Agent memories with ADK
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 … Continue reading Deploy an ADK agent to Agent Platform Runtime
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 … Continue reading Building a stock-trading agent with ADK