Why AI agents need a task queue (and how to build one)

LogRocket published guidance today on using a task queue to keep AI agents recoverable when LLM calls and side effects fail.

• It notes prompts can range from ~500 to 50,000 tokens, while LLM calls can run from ~200ms to 30+ seconds.
• It frames throttling around both requests/minute and tokens/minute limits, to reduce HTTP 429 errors during agent fan-out.
• It recommends putting full execution context (history, request, intermediate outputs) into each task so retries can reuse prior results instead of redoing work.
• It describes queue-level deduplication by rejecting tasks that match an in-flight operation with the same context and action.
• Its minimal Node.js example uses in-memory storage and defaults like 3 retries, 60 requests/min, and 90,000 tokens/min, plus priority lanes and dead-letter handling.