Building cost-effective, high-throughput gen AI workflows in Google Dataflow

The useful part

However, by integrating generative AI agents, we can move beyond static logic to adaptive execution. This allows streaming workflows to dynamically construct plans, query databases, and trigger custom remediation paths at runtime depending on the content of the data. For example, when a customer sends an angry message about a damaged order, a pipeline shouldn't just log the error or flag a dashboard.

How it works

• It should look up the order in the database that holds customer order and inventory records, decide on a remediation action (like shipping a replacement or issuing a refund), email the customer, and log the...
• However, streaming systems face a fundamental engineering hurdle when executing gen AI workflows: scale, latency, and cost.
• Sending every raw event directly to a heavyweight model or multi-step agent equipped with external database and email tools is prohibitively expensive, introduces high latency, and quickly exhausts API rate...
• By using a lightweight, CPU-bound machine learning model upstream to filter and qualify events, we keep the pip...
• This pattern addresses the scale and complexity challenge by combining Google Dataflow, Google Cloud's fully managed, serverless execution service for Apache Beam, and the Agent Development Kit (ADK) to...