Real-Time Agent Context with Kafka: Sub-Second Data Freshness for AI Pipelines
· 5 min read
Batch pipelines are sufficient for most analytical workloads. They're not sufficient for AI agents making time-sensitive decisions. An anomaly detection agent that works on yesterday's data misses the incident happening right now. A customer churn agent fed weekly snapshots can't act on a user who disengaged three hours ago.
Real-time streaming closes this gap. With Kafka as the event backbone and Flink for stream processing, your agents can operate on data that is seconds old rather than hours or days.