Best Practices for Avoiding Automation Backlogs When Using External APIs

I’m not sure I understand your question? Airtable should eventually work through the queue, but are you trying to get all of your automation runs to perform more quickly — perhaps simultaneously? It sounds like you don’t want to wait for Airtable to finish processing each lengthy automation run before moving onto the next record.

If so, I would recommend rebuilding your automation in Make’s advanced automations, and make sure that you start your automation with a custom webhook that is triggered by Airtable using my instructions in this thread: How to instantly trigger a Make automation from Airtable.

The reason for this is because when you start a Make automation with a webhook, it will run all of your automation runs SIMULTANEOUSLY — in other words, it will run the automations as quickly as the webhooks are triggered by Airtable.

You'll still have a queue in Airtable, but you’ll work through the queue much more quickly in Airtable because the lengthy part of your automation is being processed on the Make side simultaneously.

This can actually sometimes cause the OPPOSITE PROBLEM — where the automations run TOO QUICKLY — so Make offers a bunch of tools for slowing down automations, including (1) slowing down the frequency of how often a webhook will process incoming data, (2) setting the automation to run sequentially instead of simultaneously (one at a time, like Airtable), and (3) using Make’s Sleep tool to insert a pause in the automation.

If you’ve never used Make before, I’ve assembled a bunch of Make training resources in this thread, including a very helpful Loom video that I created.

I also give live demonstrations of how to use Make in many of my Airtable podcast appearances. For example, in this video, I show how to work with Airtable arrays in Make.

Hope this helps!

If you’d like to hire the best Airtable consultant to help you with anything Airtable-related, please feel free to contact me through my website: Airtable consultant — ScottWorld

Scott gave you a great solution with Make.com. Let me add some Airtable-native approaches that might help:

1. Batching Strategy
Instead of triggering on each new record, consider using a scheduled automation that runs every 5-15 minutes to process multiple records at once. Use a checkbox field like "Needs Enrichment" and filter for unchecked records. This dramatically reduces queue buildup.

2. Status Field Management
Add a single select field with statuses like "Pending", "Processing", "Complete", "Error". This helps you track which records are stuck and prevents duplicate processing.

3. Conditional Logic
Add a condition in your automation to check if the API has already been called (using a "Last Processed" date field). This prevents re-running enrichment on records that already have data.

4. Priority Queue
If some records are more urgent, add a "Priority" field and set up separate automations for high-priority vs. standard records.

5. Rate Limiting Field
Create a formula field that calculates if you're within safe API limits. Only trigger enrichment when you're under the threshold.

6. Async Pattern with n8n
Similar to Make, n8n (open source alternative) can handle webhooks and has built-in queue management. Great if you want more control or self-hosting.

The key insight: Don't fight Airtable's sequential processing. Instead, reduce the frequency of triggers and batch your API calls.

Need help implementing this? Feel free to DM me and I can share some automation templates that have worked well for similar use cases.

Hello,

Sing a scheduled automation and a "Needs Enrichment" checkbox is a brilliant, resource-friendly approach to mitigate queue buildup. Defining clear statuses like "Pending," "Processing," "Complete," and "Error" is fundamental for debugging and preventing double-processing. You hit the nail on the head: e-zpassde "Don't fight Airtable's sequential processing. Instead, reduce the frequency of triggers and batch your API calls.” These methods will significantly improve the stability and efficiency of our data enrichment process.