Anyone else looking at infinitely "Queued" Argon Claygent? This entire tabled worked fine for 400+ rows two hours ago, but for the 400 rows I just added it first shows "Queued" forever, and then crashes ("Error"). Anyone else seeing major delays in Claygent availability?
When columns get stuck on "Queued" and then error out, try these solutions: Force Rerun Column: Click the column header and select "Force Rerun Column" to reset it. Force Run Option: Click the column header, hover over "Run column," and select "Force" to jump start the column. No additional charges apply for re-running these rows. Hard Refresh: Try Ctrl + Shift + R (Windows) or Cmd + Shift + R (Mac) to refresh your browser. Create New Column: If the issue persists, duplicate the problematic column, filter for rows where the current column "is empty," run the AI on those rows, then use "merge columns" to combine results. The "Queued" issue often happens when columns are changed multiple times or runs are stopped midway.
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Casper S. Claygent runs are queuing much longer than normal and in some cases failing after extended waits. This usually points to a temporary load or availability issue on the backend. the best move is to pause runs, keep your table saved, and retry once performance stabilizes or check with support for current status updates
Yeah, I get it the inconsistency is worse than a full outage. For now, adding a retry or delay node in your n8n flow can keep the rest of the automation from breaking when Claygent hangs hopefully Clay will share a proper status update soon
I see your point, and you're not wrong. Though it kind of defeats the purpose of the automation if I need to exponentionally back off because of a column in Clay not doing what it's supposed to do, or doing it inconsistently. Lots of data is dropped when the column returns an error. Thanks for chipping in though
having to back off like that does kill the whole point of automating. dropped data from inconsistent column behavior is brutal. hopefully the Clay team can address the root cause quickly so you can run your flows without building in workarounds
