Building Cost-Effective, High-Throughput Gen AI Workflows with Google Dataflow

Real-time streaming pipelines, which are central to modern enterprise operations, traditionally followed static DAG (Directed Acyclic Graph) structures with fixed logic. However, integrating Generative AI agents enables adaptive execution that dynamically constructs execution paths based on data content and determines database queries or actions in real time.

However, passing all raw events directly to heavy models or multi-step agents results in very high costs, long latency, and rapid exhaustion of API rate limits.

To address this, we propose a hybrid streaming pipeline pattern combining Google Dataflow and the Agent Development Kit (ADK). The core mechanism is as follows:

• Pre-filter: Use CPU-based lightweight ML models upstream to filter events and validate eligibility.
• Agentic Action: Route only complex cases that pass the filter to downstream agents to perform dynamic branching.

This pattern is applicable to any domain where the majority of events are routine and only a few require complex reasoning:

• IT Operations and DevOps: Filter millions of logs, and have agents perform diagnosis and ticket creation only when critical anomalies are detected.
• Financial Fraud Detection: Inspect transactions with lightweight rules, and have agents perform multi-database lookups only for suspicious patterns.
• Industrial IoT: Monitor normal telemetry at the edge, and have agents coordinate equipment shutdowns and engineer notifications only when abnormal values occur.