engineering · 1 min read
Event-Driven AI: Wiring Workflows to Kafka, SQS and Pub/Sub
Event-driven AI scales beyond what request/response can. Here is how Bhogar AI consumes Kafka, SQS and Pub/Sub safely and at scale.
BABhogar AI TeamProduct & Engineering
Request/response patterns hit limits at high volume. Event-driven AI workflows scale further, isolate failure better and naturally support replay - provided the consumer is built right.
Why it matters
The hard part is exactly-once semantics, ordered processing and back-pressure. Production consumers must handle redeliveries idempotently and respect downstream rate limits.
How Bhogar AI approaches it
Bhogar AI ships managed consumers for Kafka, SQS and Pub/Sub with idempotency keys, ordered partitions, automatic retry with DLQ, and back-pressure that pauses the consumer rather than melting downstream services.
- Managed Kafka, SQS and Pub/Sub consumers
- Idempotency keys and exactly-once handlers
- Ordered partition processing
- DLQ with replay UI
- Back-pressure aware consumer
What you get
Streaming-AI customers handle millions of events per day per workflow without consumer lag or data loss.