Loops and iteration allow automation workflows to repeat steps across multiple items or until conditions are met. They work by tracking progress, processing each item sequentially or in batches, and handling failures gracefully. For businesses, this means reliably processing 500 records with the same care as 1. Without loops, bulk operations either fail silently or require manual intervention.
You build an automation to update 500 records in your CRM.
It works perfectly for 3 records. Then the API times out, the automation stops, and 497 records sit untouched.
Worse: you have no idea which 3 actually got updated.
Automation that cannot repeat is automation that breaks at scale.
ORCHESTRATION LAYER - Makes automation work for 1 item or 10,000.
Layer 4: Orchestration & Control | Category 4.1: Process Control
Doing the same thing until it is done
Loops and iteration let you repeat a set of steps multiple times. Sometimes you know exactly how many times: process these 500 records. Sometimes you repeat until a condition changes: keep checking until the report is ready.
The key difference from one-shot automation is state tracking. A loop knows where it is, what it has processed, and what remains. When something fails, you can resume from the failure point instead of starting over.
Every list in your business is a loop waiting to happen. Customer records. Invoice line items. Team member assignments. The question is whether you process them reliably or cross your fingers.
Loops solve a fundamental problem: how do you apply the same logic to many items without writing the logic many times? The pattern appears anywhere one action must be repeated across a set of things.
Define what to repeat. Define when to stop. Track progress as you go. Handle failures without losing work.
Click "Start Loop" to process 8 records. Watch how the loop validates each record individually and handles the invalid one gracefully without stopping.
| Status | Customer | Current Tier | New Tier | Result |
|---|---|---|---|---|
| Acme Corp | Basic | Growth | ||
| TechStart Inc | Basic | Growth | ||
| Global Foods | Growth | Scale | ||
| Data Systems | INVALID | Growth | ||
| Cloud Nine | Basic | Growth | ||
| Peak Ventures | Scale | Enterprise | ||
| Blue Ocean | Basic | Growth | ||
| Summit Labs | Growth | Scale |
Three ways to repeat work reliably
Process every item in a collection
You have a list of 200 records. The loop takes each one, applies your logic, and moves to the next. Progress is tracked automatically. If item 47 fails, items 1-46 are already done.
Repeat until a condition changes
Check a condition, do the work, check again. Keep polling until the report is ready. Keep retrying until the API responds. The loop ends when the condition becomes false.
Each step triggers the next
Instead of one loop processing everything, each successful step triggers the next iteration. Useful for long-running processes where you cannot hold state in memory.
"Update all 500 customer records with the new pricing tier"
The ops team needs to migrate customer data after a pricing restructure. Instead of 500 manual updates or a risky bulk SQL operation, a loop processes each record individually with validation, error handling, and progress tracking.
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The loop processes 200 records. At record 150, the connection drops. When you restart, it begins at record 1 again. Records 1-149 get processed twice. Some have side effects like sending emails or creating invoices.
Instead: Track which items have been processed. On restart, skip completed items. Use idempotent operations when possible.
You poll an API waiting for a report to be ready. The report generation fails silently. Your loop keeps polling forever, burning API quota and compute time until someone notices.
Instead: Always set a maximum iteration count or timeout. If the expected condition never occurs, fail gracefully with an alert.
You loop through 500 API calls as fast as possible. The API rate-limits you after 50 calls. The next 450 fail. Your error handling was not designed for bulk failures.
Instead: Add deliberate delays between iterations. Respect rate limits. Use exponential backoff when errors occur.
You do not have loops implemented in your automations.
Start with a simple for-each loop on your next bulk operation. Pick a list of 10-20 records and process them one at a time with logging enabled.
You have loops, but they fail silently or restart from scratch.
Add progress tracking to your existing loops. Log which items completed, which failed, and why. Implement a "resume from last checkpoint" capability.
Your loops work reliably and you want to scale further.
Implement parallel processing with fan-out/fan-in patterns. Add exponential backoff for API rate limits. Consider recursive processing for datasets over 10,000 items.
You have learned how to repeat operations reliably across multiple items. The natural next step is understanding how to split work across parallel paths and merge results back together.