Cost-effective gen AI workflows in Google Dataflow
Real-time streaming pipelines are the operational backbone of modern enterprises, continuously processing everything from customer support interactions to transaction logs. Traditionally, streaming DAGs are static; once deployed, their processing logic and execution paths are fixed. 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. 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 final resolution.
However, streaming systems face a fundamental engineering hurdle when executing gen AI workflows: scale, latency,...
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