This article provides a complete, practical walkthrough of the Wait Code Automation Triggered n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:  
   How to Merge Multiple Workflow Runs Into a Single Dataset with n8n
   
   Meta Description:  
   Learn how to consolidate data from multiple looped runs into a single dataset using n8n’s built-in nodes. This guide explains a practical workflow for processing and merging customer data efficiently.
   
   Keywords:  
   n8n workflow, data automation, merge loop results, automation workflow, split in batches, customer datastore, data merge n8n, noOp node, automation tools, JavaScript in n8n
   
   Third-Party APIs Used:  
   - n8n Training Customer Datastore API (n8nTrainingCustomerDatastore)
   
   Article:
   
   Efficiently Merging Multiple Workflow Runs Into One Dataset Using n8n
   
   In modern automation workflows, managing large datasets that require looping through hundreds—or even thousands—of entries efficiently is a common challenge. n8n, the powerful open-source workflow automation tool, offers a robust set of tools designed to help you process batches of data, but a problem often arises: How can you merge all intermediate loop outputs into a single, unified result?
   
   In this guide, we’ll dissect an n8n workflow specifically crafted to solve this problem. The goal of the workflow is to retrieve customer data from a datastore, process it in batches, and then combine all results into one final dataset. While it might sound complex, n8n's modular structure makes this task achievable without writing much code.
   
   Let’s walk through this step-by-step.
   
   Workflow Overview
   
   The workflow is titled “Merge multiple runs into one” and consists of several key nodes:
   
   1. Manual Trigger  
   2. Customer Datastore (Get All People)  
   3. Loop Over Items (Split In Batches)  
   4. Wait  
   5. NoOp  
   6. Done Looping? (If condition)  
   7. Merge Loop Items (Code node)
   
   Each of these nodes plays a specific role in ensuring that data is both processed and aggregated in an intelligent way.
   
   Step 1: Manual Trigger  
   The workflow begins with a Manual Trigger node, labelled “On clicking 'execute'.” This node serves as a starting point, allowing users to execute the workflow manually. It’s particularly useful during development and testing.
   
   Step 2: Fetching Data from Customer Datastore  
   Next, the workflow queries a custom datastore using the n8nTrainingCustomerDatastore node. The operation used is “getAllPeople,” which fetches the entire list of customer records. This API is a mock interface intended for training or demo purposes, but the logic can be adapted to any real data source with a similar structure.
   
   Step 3: Splitting Data for Loop Processing  
   To prevent performance bottlenecks and enable manageable data processing, the customer records are passed to a SplitInBatches node named “Loop Over Items.” This node breaks down the data into smaller, more manageable chunks. The loop’s design allows processing each subset of data individually.
   
   Step 4: Adding a Delay Between Loops  
   Inside the loop, a Wait node introduces a tweakable delay to simulate asynchronous operations such as API calls, rate limits, or heavy processing. This can be particularly useful for avoiding service throttling or request denials.
   
   Step 5: NoOp Node and Loop Checking  
   After each batch of data is processed (simulated by the Wait node), the workflow proceeds through a NoOp node—essentially a placeholder node used when no additional operations are needed. The route then hits an If condition node titled “Done looping?” to check a crucial context variable: noItemsLeft. This indicates whether all the looped data has been processed.
   
   Step 6: Conditional Logic for Loop Termination  
   The “Done looping?” node checks if all batches have been processed. If no items are left, the next logical step is to merge the data. If not, it loops back to process the remaining items.
   
   Step 7: Merging Results with Code Node  
   The magic of combining all loop executions happens in the “Merge loop items” node—a JavaScript-powered code block. This code dynamically fetches and concatenates all data outputs from previous loop executions by tapping into n8n’s execution context.
   
   The code used looks like this:
   
   let results = [],  
     i = 0;
   
   do {  
     try {  
       results = results.concat($("NoOp").all(0, i));  
     } catch (error) {  
       return results;  
     }  
     i++;  
   } while (true);
   
   This loop continues fetching results from the NoOp node (effectively tracing back loop outputs) until it encounters an error, which is interpreted as “no more results to fetch.” At this point, a complete dataset of all previously processed items is returned.
   
   Why This Workflow Matters
   
   Any automation professional knows that handling data in chunks may be necessary due to resource constraints or API limits. However, merging those results into something centrally useful places an additional layer of complexity on the workflow design.
   
   This n8n workflow elegantly solves that hurdle using a combination of looping tools and a single custom script—resulting in a clean, efficient, and scalable solution.
   
   Use Cases and Adaptability
   
   Though built using a mock customer datastore, this structure can be adapted for various applications, such as:
   
   - Paginated API responses  
   - Batch email sending  
   - Survey or form response processing  
   - Bulk data updates in CRM systems  
   
   Final Thoughts
   
   This workflow showcases one of n8n's most powerful capabilities—its flexibility. By making use of native nodes such as SplitInBatches, If conditionals, and Code nodes, users can design complex data pipelines that are both efficient and easy to manage.
   
   Whether you're automating advanced processes or simply want to improve data handling practices, understanding how to aggregate and manage loop outputs effectively is a must—and this workflow provides a perfect blueprint to get started.
   
   By incorporating just a touch of custom logic, you can dramatically extend the utility of your workflows without compromising on performance or clarity.
   
   Happy automating!
   
   —  
   Author: Your AI Workflow Assistant  
   Date: [Insert current date]

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.