Aggregator in Automation

Some automation steps do not process one record at a time. They collect many records and combine them into a single output. That is what an aggregator does.

An aggregator in automation gathers multiple data items from a loop or a set of results and merges them into one usable output, like a summary, a table, or a combined value.

Key Takeaways

• Aggregator combines data: it takes multiple separate items and merges them into a single structured output.
• Used after iterators: aggregators typically follow a loop or iterator step that processes items one at a time.
• Common in reporting: summarizing totals, building lists, and compiling tables all use aggregation.
• Output types vary: depending on the platform, aggregators can output arrays, text strings, tables, or numbers.
• Not the same as a filter: aggregators combine data; filters remove data that does not match conditions.

What Is an Aggregator in Automation?

An aggregator in automation is a step that collects multiple separate data items processed through a loop and combines them into a single output, such as an array, a sum, a text block, or a formatted table.

Imagine your workflow pulls every order from the last 24 hours one by one. An aggregator takes all those individual order records and compiles them into a single summary before sending a daily report email.

• Combines loop output: gathers all the items produced during an iteration and merges them into one.
• Ends a loop: in platforms like Make, the aggregator is what closes an iterator loop and produces the final combined result.
• Single downstream output: instead of passing fifty separate records to the next step, the aggregator passes one structured dataset.

Aggregators are one of the most useful but least understood tools for anyone moving beyond simple single-trigger, single-action workflows.

How Does an Aggregator Work in a Workflow?

An aggregator works by waiting for an iterator loop to finish processing all items, then collecting every item's output and merging it according to its configured rules, such as joining text, summing numbers, or building an array.

The sequence is always: iterator runs across each item, then aggregator collects the results and passes one combined output to the next step.

• Iterator runs first: the workflow processes each item in a dataset individually through a loop.
• Aggregator collects outputs: after each loop iteration, the aggregator stores that item's result.
• Loop ends, aggregation finalizes: when all items are processed, the aggregator merges everything into the configured output format.
• Single bundle passes forward: the next step in the workflow receives one clean output instead of many separate bundles.

This pattern is essential when your downstream step expects a single input, such as an email body or a database record, not a stream of individual items.

What Are the Common Types of Aggregators in Automation?

Common aggregator types include text aggregators that join strings, numeric aggregators that sum or average values, array aggregators that collect items into a list, and table aggregators that build formatted rows from multiple records.

Different aggregation needs require different aggregator types. Choosing the right one depends on the output your next step expects.

• Text aggregator: joins individual text outputs into one block, useful for building email bodies or document content.
• Numeric aggregator: calculates totals, averages, minimums, or maximums from a set of numeric values.
• Array aggregator: collects individual items into a structured array that downstream steps can loop through or process as a list.
• Table aggregator: compiles multiple records into a formatted table, often used before sending a report or writing to a spreadsheet.

In Make (formerly Integromat), the array aggregator is one of the most widely used tools for handling multi-record workflows without custom code.

When Should You Use an Aggregator in Automation?

Use an aggregator whenever your workflow processes multiple items in a loop and the next step needs one combined output. If you want to send a single email with all results, build a summary report, or write a batch to a database, an aggregator is the right tool.

Not every workflow needs an aggregator. Use one only when the downstream step expects a single input rather than a stream.

• Batch reporting: collect daily sales, support tickets, or inventory changes into one digest email or dashboard update.
• Bulk database writes: aggregate individual records into a batch insert instead of writing one record at a time.
• Document generation: compile multiple data points into a single formatted document from a template.
• Summary calculations: sum totals, count records, or average values from a loop before displaying or storing the result.

At LOW/CODE Agency, we use aggregators regularly in reporting workflows where data comes from multiple sources and needs to be presented as one coherent output.

What Is the Difference Between an Aggregator and an Iterator?

An iterator splits one bundle containing multiple items into separate bundles, processing each one individually. An aggregator does the opposite: it collects those individual bundles back into one. They are typically used together in a loop structure.

Understanding which direction data flows helps you place these tools in the right order in your workflow.

• Iterator direction: one-to-many. Takes one input with multiple items and creates one bundle per item.
• Aggregator direction: many-to-one. Takes many individual bundles and merges them into one output.
• Common pattern: iterator opens the loop, processes each item, aggregator closes the loop and produces the final result.
• Neither is optional: if you open a loop with an iterator, you almost always need an aggregator to close it cleanly.

Learning to work with iterators and aggregators in Make opens up multi-record automation patterns that are impossible with simple single-item workflows.

Conclusion

An aggregator is the step that turns many separate results into one usable output. It is essential for any workflow that processes a list of items and needs to deliver a combined result downstream. Once you understand how iterators and aggregators work together, multi-record automation becomes significantly more manageable.

Need Automation That Handles Complex Data Without Breaking?

Multi-record workflows fail silently when aggregation is set up wrong. Mismatched formats, missed items, and empty outputs are common problems teams discover in production.

At LOW/CODE Agency, we design data aggregation workflows with proper loop structure, error handling, and output validation. We have built 450+ automation systems for clients including Sotheby's, Medtronic, and American Express.

• Loop architecture design: we map the full iterator-to-aggregator path before building any multi-record workflow.
• Output format validation: we confirm aggregated outputs match exactly what downstream steps expect.
• Error handling in loops: failed iterations are caught and logged without breaking the entire aggregation run.
• Batch optimization: we minimize API calls by aggregating before writing, not writing one record at a time.
• Reporting automation: we build daily, weekly, and real-time summary workflows that aggregate data across multiple connected apps.

If your automation needs to process lists of data and produce clean combined outputs, let's talk.