To design a data pipeline for agentic AI, follow these steps:

• Data Collection: Use Snowplow's trackers to collect real-time data on user actions, system states, and external events
• Data Processing: Clean, enrich, and transform raw event data to ensure it's suitable for decision-making. Tools like dbt or Spark can be used for transformation
• Real-time Streaming: Use tools like Kafka, Kinesis, or Flink to stream data into your agentic AI system in real time
• Action Execution: Once data is processed, pass it to the AI system for decision-making and action execution. This can involve triggering workflows, alerts, or system updates

Snowplow Signals simplifies this architecture by providing a unified system that combines streaming and batch processing, delivering real-time customer attributes through APIs that agentic AI systems can easily consume.