This article provides a complete, practical walkthrough of the Manual Splitinbatches Automate 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:  
   Automating Batch Processing with Conditional Execution Using n8n
   
   Meta Description:  
   Discover how to create an efficient and dynamic batch processing workflow in n8n using the Split in Batches and IF nodes. Learn step-by-step logic for automating conditional actions triggered by a manual event.
   
   Keywords:  
   n8n, n8n workflow, automation, batch processing, no-code automation, SplitInBatches node, IF node, JavaScript function in n8n, manual trigger workflow, workflow automation, automation logic, n8n tutorial
   
   Third-Party APIs Used:  
   None – this workflow operates entirely within native n8n nodes and does not interact with any external APIs.
   
   Article:
   
   Automating Conditional Batch Processing in n8n: A Step-by-Step Guide
   
   No-code and low-code platforms like n8n make automation accessible to people with minimal programming experience. Among its powerful built-in tools, n8n offers a simple yet effective way to manage data chunks and conditional logic to build flexible workflows. In this article, we walk through a practical example of using n8n to generate a list of items, process them one at a time in batches, and take an action when all items are exhausted.
   
   This tutorial showcases the power of modular automation by leveraging key n8n nodes such as Manual Trigger, Function, SplitInBatches, IF, and Set. Let’s break down this workflow and understand the logic powering it.
   
   Workflow Overview
   
   Here’s a high-level summary of what this n8n workflow does:
   
   1. Starts manually via a trigger.
   2. Generates an array of 10 items (indexed 0–9).
   3. Iterates through each item, processing them one at a time.
   4. Checks if there are no items left to process.
   5. If true, it sets a message “No Items Left.”
   
   Let’s dive into each part:
   
   Manual Trigger: Simplicity at Its Core
   
   The workflow begins with the Manual Trigger node titled “On clicking 'execute'.” This node kickstarts the workflow manually, usually during development or testing. It’s extremely useful for running automations without relying on external triggers.
   
   Function Node: Dynamically Generating Data
   
   The second node is a Function node, which uses JavaScript to create an array of 10 items. Each item is a simple JSON object containing a single key, i, representing an index from 0 to 9. Here's the JavaScript snippet used:
   
   ```javascript
   const newItems = [];
   
   for (let i = 0; i < 10; i++) {
     newItems.push({ json: { i } });
   }
   
   return newItems;
   ```
   
   This mimics a scenario where you need to process 10 different records or data entries sequentially. Using a Function node here provides flexibility—data could be fetched from an API, generated on the fly, or read from a file in a real-world setup.
   
   SplitInBatches Node: Controlling the Workflow Pace
   
   Post generation, this list of items moves to the SplitInBatches node. This powerful node allows iteration through items in specified batch sizes. In this case, batchSize is set to 1, meaning each item will be processed individually—ideal for tasks like sending emails, submitting API requests, or saving data entries one-by-one.
   
   Additionally, SplitInBatches keeps a useful context called noItemsLeft that becomes true when the last item in the list has been processed. This acts as a flag for controlling workflow continuation, which leads us to the next part of the workflow.
   
   IF Node: Decision Making in Automation
   
   The IF node inspects the SplitInBatches node’s context to determine whether the batch processor has finished processing all items. The condition it evaluates is:
   
   ```javascript
   true === {{$node["SplitInBatches"].context["noItemsLeft"]}}
   ```
   
   If this conditional logic returns true, that means all batches have been processed.
   
   On true outcome, the workflow continues to a Set node to declare “No Items Left.” If not, the workflow loops back to process the next item in the batch by connecting back to the SplitInBatches node.
   
   This dynamic loop—created by a back-connection from the false branch of the IF node—demonstrates how n8n supports iterative logic, almost like programming a classic while loop but with workflow-friendly visuals.
   
   Set Node: Sending a Message Upon Completion
   
   Once all items are processed, a Set node comes into play. This node creates a key-value pair that simply adds a confirmation message:
   
   ``` 
   Message: No Items Left 
   ```
   
   While this doesn’t perform an external action, it easily can. You could replace the Set node with a Send Email node, Webhook, Slack notification, or a custom API call to alert someone or trigger another workflow.
   
   Use Cases and Flexibility
   
   This workflow structure, while basic in this demo, has many real-world applications:
   
   - Batch-processing data entries from a database.
   - Sending out bulk notifications or emails sequentially.
   - Adding delay and retry mechanisms in batch tasks.
   - Dynamically tracking the end of a repetitive process.
   
   Since everything operates using n8n’s native core nodes, it’s efficient and doesn’t rely on paid third-party apps or APIs. This lowers system fragility and eases maintenance over time.
   
   Conclusion
   
   This n8n workflow may look simple at first glance, but it’s a great demonstration of how much power and flexibility the platform offers for building intelligent automations. By combining functional programming concepts with conditional logic, you can have full control over how data is processed—and what happens next.
   
   Whether you're a no-code enthusiast or an automation pro, understanding how to utilize SplitInBatches and IF nodes unlocks new ways to scale your workflows purposefully without overcomplicating your logic.
   
   Now that you’ve mastered this pattern, think about how you might extend it: hook it up to a Telegram bot, add API integration, or write results to a Google Sheet. The possibilities are as scalable as your imagination.
   
   Happy Automating!

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.