This article provides a complete, practical walkthrough of the Wait Code Update 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:  
   Building Asynchronous Sub-Workflows in n8n with Parallel Execution and Webhook Callbacks
   
   Meta Description:  
   Learn how to orchestrate asynchronous parallel sub-workflows in n8n using webhook callbacks and execution resumes. This guide explores pseudo-synchronous waiting, callback handling, and error mitigation strategies for distributed automation.
   
   Keywords:  
   n8n, parallel workflows, webhook callback, asynchronous workflows, workflow orchestration, sub-workflow, automation, HTTP request, process sync, callback URL
   
   Third-Party APIs Used:  
   - None directly referenced. This workflow uses HTTP Request and Webhook nodes for internal communication between workflow instances. However, the structure can be adapted to call third-party APIs.
   
   —
   
   Article:
   
   In modern workflow automation, it’s not uncommon to encounter use cases that require the orchestration of multiple asynchronous tasks that eventually synchronize into a cohesive process. Enter n8n — a powerful tool that lets you build scalable and modular workflows. In this article, we explore a practical and advanced use case where n8n launches multiple sub-workflows in parallel and then waits for all of them to complete before continuing — using asynchronous webhook communication and a pseudo-synchronous execution loop.
   
   We’ll take a detailed look at how this is achieved in a no-code/low-code fashion.
   
   🧠 The Problem: Asynchronous, Yet Controlled
   
   Suppose you need to process multiple items (e.g., data entries, tasks, or API requests) simultaneously. Each “item” follows its own processing logic, but the parent workflow shouldn’t move forward until all "children" have completed their work. This is especially useful in ETL pipelines, task queues, or workflows deploying microservices.
   
   The core challenge lies in managing state and communication between the main workflow and its sub-workflows. n8n doesn't naturally support true async job coordination because each workflow execution is sandboxed — meaning we need a clever workaround.
   
   🧰 The Solution: Parent-Child Workflow Execution with Webhook Callbacks
   
   Let’s deconstruct the solution provided by the featured n8n workflow, which uses a combination of HTTP Requests, looping, and webhook waits to create a synchronized execution model.
   
   Step 1: Trigger and Simulate Items  
   The workflow kicks off manually using a Manual Trigger node. This is followed by the Simulate Multi-Item for Parallel Processing Code node which returns an array of mock “request items,” for instance:
   
   ```js
   [
     { requestId: 'req4567' },
     { requestId: 'req8765' },
     { requestId: 'req1234' }
   ]
   ```
   
   Each of these items will act as an independent task passed down to a sub-workflow.
   
   Step 2: Parallelizing Execution  
   The Loop Over Items node (using SplitInBatches) passes each request ID into a HTTP Request node called Start Sub-Workflow via Webhook. Here, a POST request is made to a predefined sub-workflow webhook path. Crucially, this request includes a custom header: callbackURL — which contains the parent workflow’s resume URL. This ensures each sub-task has a way to notify its parent once completed.
   
   Key HTTP Request values:
   - URL: webhook for sub-workflow
   - Body: includes requestItemId
   - Header: includes callbackURL for parent
   
   Step 3: Sub-Workflow Logic  
   The sub-workflow (defined separately and activated) listens for the POST request, processes the input, and after a brief Wait node (simulating async processing), calls back the parent via the callback URL it received.
   
   The structure is:
   1. Webhook Listener
   2. Respond to Webhook immediately (to acknowledge receipt)
   3. Wait node to simulate processing time
   4. HTTP Request back to parent (callback) with identifier of the completed sub-task
   
   This call resumes the paused parent workflow via its Wait node (Webhook Callback Wait).
   
   Step 4: Resuming and Tracking  
   When the parent receives the POST callback, it:
   - Acknowledges the communication
   - Extracts the finishedItemId
   - Pushes this into a set called finishedSet via Update finishedSet node (Code node)
   
   This step is repeatable — it loops back and waits again until all sub-workflows report back.
   
   Step 5: Synchronization Check  
   After each callback, the If All Finished node compares the length of finishedSet with the number of simulated items. If they match, the workflow concludes and continues. If not, it loops back to wait again for more callbacks.
   
   This use of a pseudo-loop with webhook resumption creatively solves n8n’s lack of built-in async job tracking across multiple workflow executions.
   
   📝 Notes and Considerations
   
   - Ensure your n8n instance has the internal webhook base URL configured correctly (e.g., using a Kubernetes service name) so the callback URLs are reachable.
   - Implement retry mechanisms in the sub-workflow’s callback logic (already included in this design using retryOnFail with delay).
   - Be aware of possible race conditions if multiple sub-workflows signal nearly simultaneously. The retry-and-wait strategy mitigates this to a reasonable degree.
   
   🚀 Final Thoughts
   
   This n8n workflow exhibits an elegant pattern for handling multiple parallel processes asynchronously while maintaining visibility and resumption control in the parent logic. It leverages:
   - Sub-workflows initiated via HTTP
   - Webhook-based state tracking
   - Conditional logic for flow continuation
   - Retry strategies to ensure callback delivery
   
   While no external APIs are used here, the same structure can be applied to real-world async scenarios — such as calling APIs, processing large data sets, or automating multi-user workflows.
   
   n8n’s node flexibility and HTTP/Webhook support make it an excellent orchestration platform once you understand how to structure flows for loosely coupled, asynchronous task handling.
   
   Whether you're building data pipelines or automating operational tasks, this approach will deepen your understanding of distributed event coordination within n8n.
   
   Give it a try, and 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.