Kafka & Event Streaming
Agents that poll are toys; agents that react are infrastructure. When a claim lands, a ticket escalates, or a transaction flags, the agent should already be working — which means your AI layer needs what your microservices needed a decade ago: an event backbone that delivers reliably, in order, at scale.
We build event-driven agentic systems on Kafka and its relatives: triggers that wake agents on real business events, streaming pipelines that keep retrieval indexes current in near-real-time, and consumer patterns — idempotency, dead-letter queues, backpressure — that keep an agent hiccup from becoming a data-loss story. Streaming discipline is old; pointing it at agents is new. We speak both.
Event-driven agent triggers
Agents that react to business events in seconds, not on a cron schedule.
Streaming ingestion for retrieval
CDC and event pipelines that keep vector indexes and caches continuously fresh.
Reliable consumer patterns
Idempotent processing, dead-letter queues, and replay — agent failures without data loss.
Kafka platform operations
Deployment, partitioning strategy, and monitoring on Kubernetes or managed services (MSK, Confluent).
Event schema governance
Schema registry and versioning so producers and consumers evolve without breakage.
Do we need Kafka for agents, or is a queue enough?
If one consumer processes jobs, a simple queue — even Postgres — is enough, and we'll say so. Kafka earns its complexity when multiple systems consume the same events, when you need replay, or when volume is genuinely high. We size the tool to the problem, not the résumé.
What happens when an agent consumer fails mid-event?
Nothing dramatic — that's the point of the patterns. Offsets don't advance until processing commits, retries are idempotent, and poison events route to a dead-letter queue with alerting. The stream keeps flowing; the failure gets handled instead of hidden.
Can you retrofit event-driven triggers onto our existing systems?
Usually, yes — change-data-capture (Debezium and friends) turns database writes into event streams without touching legacy application code. Your thirty-year-old system of record starts emitting events, and your agents start reacting to it, no rewrite required.
Put Kafka & Event Streaming to work — in production.
One forward-deployed engineer, embedded in your stack, owning the outcome from discovery to production. Weeks, not quarters.
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