How Redis Streams and Idempotent Workers Fix Broken AI Agent Polling Loops

Most production AI agents rely on polling loops that repeatedly query databases or APIs at fixed intervals, a pattern that breaks down at scale. This approach causes problems including high tail latency, cascading API rate limits, wasted compute resources, and race conditions when multiple workers process the same task simultaneously. An event-driven architecture using Redis Streams addresses these issues by having the environment push enriched event payloads to agents instead of agents continuously pulling for work. Consumer groups distribute incoming events across workers, while idempotency checks using atomic Redis operations prevent duplicate processing. The result is a reactive, low-latency workflow that reduces infrastructure overhead and ensures reliable execution under heavy load.