Real-time platforms can detect an important event in seconds, while AI agents can interpret context and take action, but connecting the two carelessly can create expensive, unpredictable automation loops. This session presents an event-to-action architecture for deciding when deterministic rules are enough, when an AI agent should reason, and when a human must approve the next step. Using a representative operational scenario, I will trace the flow from streaming ingestion and event detection through semantic enrichment, agent reasoning, tool execution, and feedback. We will examine patterns for event correlation, ontology-driven context, rule-versus-agent routing, confidence thresholds, idempotent actions, approval gates, and audit trails, along with observability for latency, cost, failed actions, and model uncertainty. I will also show how to prevent duplicate or conflicting actions when multiple events arrive at once and how to preserve business context as the workflow crosses systems. Attendees will leave with a decision framework and reference pipeline for turning continuously arriving data into timely AI-assisted action while keeping automation bounded, explainable, and operationally safe.

Interests

AI Application Development
MLOps & AIOps
AI Transformation
Data Architecture

Job Functions

Architecture/Design
AI Engineer
Data Engineer
IT Operations