Agentic AI systems require a wide variety of data to function effectively, including:

• Real-time event data: Tracking user interactions, environmental variables, and external system data to inform decisions
• Historical data: Learning from past behaviors, decisions, and outcomes to optimize future actions
• Contextual data: Understanding the context of decisions (e.g., time, location, user state) to make appropriate responses
• Feedback data: Continuous feedback on actions taken to fine-tune and improve future decisions

Snowplow's event-tracking capabilities provide the real-time data necessary for agentic AI systems to operate autonomously and intelligently. Snowplow Signals further enhances this by computing real-time user attributes and delivering AI-ready customer intelligence through low-latency APIs.