In the previous post we gave the agent a memory by calling add_session_to_memory() in an after-agent callback. That works, but it leans on an embedding model to decide what’s worth keeping from each session — which makes it a little non-deterministic. Say “remember that …” and it … 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
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 … 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. ADK deployments are typically deployed using a small Python script to configure the required properties. Create the following script in the root project … Continue reading Deploy an ADK agent to Agent Platform Runtime
The Agent Development Kit (ADK) is an open-source framework from Google designed to simplify building, evaluating, and deploying AI agents. It allows developers to define agent logic, connect external tools, and orchestrate workflows directly in Python. I wanted a slightly more … Continue reading Building a stock-trading agent with ADK