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# replication-ask-ai
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I’m tuned to answer only Airbyte-related questions, and the provided knowledge sources only cover Airbyte + Temporal + Prefect interactions, not general Prefect “Late” flow debugging. From the sources, the only relevant overlap is a case where Prefect flows calling Airbyte got
httpx.ReadTimeout
/
httpcore.ReadTimeout
when triggering Airbyte syncs, which caused Prefect retries and failures. The underlying problem there was on the Airbyte/Temporal side (Temporal resource exhaustion and “Dropped long duration scheduled task” messages), not Prefect itself.[timeout errors] If your “Late” flows are specifically the ones orchestrating Airbyte syncs, you’ll want to: 1. Check Airbyte’s Temporal logs for: ◦
consistent query buffer is full, this may be caused by too many queries and workflow not able to process query fast enough
◦
Dropped long duration scheduled task.
These indicate Temporal is overloaded or misconfigured and can cause slow or failed responses to Prefect.[timeout errors] 1. Look at the Airbyte job logs for those connections to see if jobs are: ◦ Failing with “Something went wrong within the airbyte platform” ◦ Hitting retry limits like: `Job failed after too many retries for connection ...`[timeout errors] 1. Compare flows that are “Late” vs. healthy: ◦ Are the “Late” ones all calling the same Airbyte instance / workspace / connection? ◦ Do they line up in time with spikes in Temporal “buffer is full” or “Dropped long duration scheduled task” messages? If your question is about Prefect “Late” status in general (not in the context of Airbyte), the knowledge sources don’t contain enough information to explain or fix that, and I can’t reliably guide you beyond the Airbyte-related angle. 👍 0 button 👎 0 button ℹ️ Tip: Mention @kapa.ai in the thread for follow-ups or DM the app for 1-to-1 help.