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, 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 limits.
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 build a hybrid streaming pipeline. By using a lightweight, CPU-bound machine learning model upstream to filter and qualify events, we keep the pipeline highly cost-effective, routing only the complex cases to the downstream agent. There, the agent dynamically decides what actions to take, introducing dynamic branching to the stream without hardcoding thousands of conditional steps into the pipeline’s static DAG.
A universal blueprint for high-volume streams
While we use a customer support triage scenario below, this pre-filter + agentic action pattern is a universal paradigm. It applies to any stream where a high volume (>9X%) of events are routine, and only a small number require complex, contextual reasoning.
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IT Operations & DevOps: Filtering millions of routine system logs on CPU, and triggering an agent to run diagnostics and open bug tickets only when a critical anomaly is flagged.
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Financial Fraud Triaging: Passing millions of transactions through lightweight, local rules, and calling an agent to execute multi-database lookup tools only for highly suspicious patterns.
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Industrial IoT: Monitoring normal telemetry on the edge, and routing erratic spikes to an agent to coordinate equipment shutdowns and email field engineers.
The architecture: Why pre-filter streaming events?
In a high-throughput stream, the vast majority of messages do not require complex reasoning or remediation. They might be positive feedback, neutral inquiries, or simple queries.
Routing every single event to a heavyweight LLM workflow creates three primary bottlenecks:
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API cost: Frontier models charge per token. Under high throughput, cost scales linearly with stream volume.
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Latency: Multi-step workflows (which involve database lookups and external API calls) take seconds, creating a bottleneck in streaming DAGs.
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Quotas: External APIs have strict rate limits that streaming workers can easily exhaust.
To prevent this, we build a pre-filtered pipeline in Apache Beam/Dataflow:
