This article provides a complete, practical walkthrough of the Wait Manual Automation Webhook 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 Customer Data Posting with n8n: A Workflow Using Batches and Delays
   
   Meta Description:
   Learn how to automate the process of sending customer data to an external API using n8n. This tutorial breaks down a workflow that retrieves data, processes it in batches, and includes delay handling for smoother HTTP operations.
   
   Keywords:
   n8n workflow automation, n8n SplitInBatches, HTTP Request n8n, API automation, customer data sync, n8n tutorial, no-code ETL, data integration, JSONPlaceholder, n8n Wait node
   
   Third-Party APIs Used:
   - JSONPlaceholder (https://jsonplaceholder.typicode.com): A free fake REST API used here to simulate HTTP POST requests.
   
   Article:
   
   Automating Customer Data Posting with n8n: A Workflow Using Batches and Delays
   
   As businesses grow, automating repetitive data tasks becomes not only useful but essential. No-code and low-code platforms like n8n make this easier by allowing you to create data workflows that operate automatically or at the click of a button. In this article, we’ll explore an n8n workflow example that retrieves a list of customers, processes them one by one, sends their data to an external API, and includes a delay between requests to avoid rate-limiting or server overload.
   
   Let’s break down the purpose and functionality of each part of this seven-node n8n workflow.
   
   1. Manual Trigger: Start On-Demand
   
   The entry point of this workflow is the Manual Trigger node named "On clicking 'execute'." This allows the user to manually start the workflow during testing or on an as-needed basis. It’s a common starting point for development and debugging since it eliminates the need for an external trigger like a webhook or scheduled cron job.
   
   2. Customer Datastore: Pulling Data from the Source
   
   Next up is the "Customer Datastore" node, simulating a typical scenario of pulling customer records from a data source—in this case, a mock node that fetches all available customer entries.
   
   This node acts as a placeholder for your actual CRM or database integration. It retrieves every customer in the dataset at once because the parameter returnAll is set to true.
   
   3. SplitInBatches: Processing One by One
   
   To avoid hitting API rate limits or overloading the receiving server, the workflow uses the SplitInBatches node. This is a powerful way to process large datasets in smaller chunks. In this example, the batch size is set to 1: each customer will be processed individually. This plays a crucial role when wiring it up with delays.
   
   4. HTTP Request: Sending Data to the API
   
   Once the data is in manageable batches, the HTTP Request node sends each customer’s ID and name to an external API endpoint. Here, we use https://jsonplaceholder.typicode.com/posts, a free tool for API testing and prototyping.
   
   Each request uses the POST method and sends the following parameters in the body:
   
   - id: The customer's ID
   - name: The customer’s full name
   
   Because the values are dynamically inserted using expressions (e.g., {{$json["name"]}}), each request reflects the specific customer in that batch.
   
   5. Wait Node: Adding a 4-Second Delay Between Requests
   
   To ensure spacing between each API call, the workflow incorporates a Wait node configured to pause for 4 seconds before proceeding to the next record. This is particularly useful when working with APIs with rate limits, or instable servers that may fail under rapid interactions.
   
   The Wait node is connected back to SplitInBatches, creating a loop. After each customer’s data is sent via HTTP, the Wait node holds execution, then the next batch (a single customer) is pulled for processing.
   
   6. Replace Me: Placeholder for Future Actions
   
   The final node is a NoOp node titled "Replace Me"—a placeholder for future workflow steps. You could replace it with additional logic, such as updating a local database, logging a success notification, or sending a message via Slack to signal that the record was processed.
   
   This provides modularity and extensibility; as your processes evolve, this node can be swapped out for more meaningful action.
   
   The Loop in Action
   
   The intelligent design of connecting HTTP Request to both the "Wait" and the "Replace Me" nodes allows the workflow to accomplish two tasks simultaneously:
   
   - Logging or post-processing the customer data sent.
   - Looping back to the Wait node, which in turn re-triggers the SplitInBatches process.
   
   This kind of looping is common in n8n when you want to maintain operational logic between cycles without any external triggers.
   
   Use Cases and Benefits
   
   Here’s why this kind of setup is useful:
   
   - API Rate Limit Compliance: Some APIs throttle requests. The Wait node avoids rate-limit violations.
   - Modular Design: With placeholder nodes like "Replace Me," your process can grow with your needs.
   - Batch Efficiency: n8n's SplitInBatches makes it simple to work through large datasets over time.
   - Rapid Prototyping: Using a test API like JSONPlaceholder lets developers prototype workflows in safe environments.
   
   Conclusion
   
   This simple yet powerful n8n workflow is an excellent example of how modern automation tools make it easier to integrate systems, process data predictably, and mitigate risks like server overload or rate limiting. Whether you're connecting to a CRM, an e-commerce platform, or an internal datastore, this pattern of manual trigger → retrieve → batch → process → delay → repeat is reusable across many business scenarios.
   
   So next time you need to send customer data to an external endpoint while staying compliant with API restrictions, give this workflow setup a shot—your future self will thank you.
   
   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.