This article provides a complete, practical walkthrough of the Code Respondtowebhook Process Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Expert setup in 2-4 hours. One‑time purchase: €149.

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:  
   Secure WhatsApp Flow Data Processing with n8n: A Complete Hybrid Encryption Workflow
   
   Meta Description:  
   Discover how this powerful n8n workflow securely decrypts and processes WhatsApp Flow data using hybrid RSA-AES encryption to dynamically handle user inputs such as appointment schedules and seat selections.
   
   Keywords:  
   n8n workflow, WhatsApp Flows, RSA AES hybrid encryption, WhatsApp data security, webhook automation, data decryption, WhatsApp chatbot backend, WhatsApp appointment scheduling, encrypted payload processing, n8n code node
   
   Third-Party APIs Used:  
   There are no external third-party APIs explicitly used in this n8n workflow. All processing is handled internally with custom JavaScript nodes and n8n's built-in components. However, this workflow is designed to receive encrypted payloads likely originating from WhatsApp Flows, meaning WhatsApp's platform (through Meta) is an implicit external system interacting with this n8n instance via POST webhook.
   
   Article:
   
   —
   
   Securing WhatsApp Flow Interactions with n8n: A Hybrid Encryption Workflow for Smart Scheduling
   
   In the age of conversational business platforms like WhatsApp, secure and responsive user experiences are critical. But what happens behind the scenes when a user interacts with a WhatsApp Flow—perhaps picking an appointment time or selecting a seat during booking?
   
   This is where low-code platforms like n8n shine. In this article, we break down a robust n8n workflow that listens for encrypted WhatsApp Flow data, decrypts it securely using hybrid RSA-AES methods, intelligently processes user input, and returns encrypted responses tailored to their journey step—all without requiring external integrations.
   
   Let’s explore how this is achieved.
   
   🔐 Step 1: Receiving and Decoding Encrypted Data via Webhook
   
   It all begins with the Webhook1 node, which listens for HTTP POST requests from WhatsApp Flows. These requests carry a payload encrypted with hybrid encryption:
   
   - RSA for securely transmitting a session-specific AES key.
   - AES-GCM for securely encrypting the actual message data.
   
   Upon receipt, the move to base64 node converts base64-encoded input (`encrypted_aes_key`, `encrypted_flow_data`, and `initial_vector`) into binary buffers the decryption algorithm can use.
   
   The Decryption Code node then accepts this data and securely reconstructs the message:
   
   - The RSA private key decrypts the AES key.
   - AES-GCM decrypts the encrypted payload.
   - The decrypted JSON payload becomes available for intelligent parsing.
   
   🧠 Step 2: Understanding the User’s Intent
   
   With clear-text data now available, the Json Parser node processes the payload. It extracts key fields such as:
   
   - The “screen” the user is currently on
   - User input data (e.g., selected date, appointment, seat)
   - The session-based flow_token
   
   This parsing is crucial in adapting behavior based on where the user is in the flow.
   
   🧭 Step 3: Routing Based on Flow Context
   
   Now enters the Switch node, acting as the decision-maker.
   
   Depending on the screen value in the parsed payload, the workflow branches:
   
   - If screen == "APPOINTMENT", it processes available appointment times.
   - If screen == "DATE_SELECTION_SCREEN", it processes seat selections.
   
   This dynamic branching allows the workflow to adapt quickly to a user’s place in the conversational experience.
   
   📆 Step 4: Handling Appointments and Seat Selections
   
   Each path has a specific data-processing subroutine:
   
   - For "APPOINTMENT": The Data Extraction Code aggregates available appointment times by date. These are mapped into structured JSON objects suitable for presentation.
   - For "DATE_SELECTION_SCREEN": The Data Extraction Code1 node extracts available seats from the payload and organizes them for display.
   
   These steps enable users to see relevant and dynamic booking options right in their WhatsApp interface.
   
   🔁 Step 5: Encrypting the Response
   
   Once the appropriate data is processed, it's time to securely send it back.
   
   The Encrypt Return and Encrypt Return1 nodes accomplish this by:
   
   1. Flipping the Initialization Vector (IV) for added obfuscation.
   2. Encrypting the structured JSON response with AES-GCM, using the session-specific AES key.
   3. Appending an authentication tag for data integrity.
   4. Encoding the secured message in base64.
   
   These encrypted packets are returned to WhatsApp via Respond to Webhook1 or Respond to Webhook2.
   
   📝 Wrapping It All Together
   
   Here’s a high-level overview of the complete workflow:
   
   1. Webhook receives encrypted WhatsApp Flow data.
   2. Data is converted to a useable binary format.
   3. RSA decryption extracts the AES key.
   4. AES key decrypts the flow data securely.
   5. Parsed content reveals current step (or “screen”) in WhatsApp Flow.
   6. A Switch node routes to logic specific to that screen.
   7. Data is grouped (times/dates or seats).
   8. Response is encrypted using AES-GCM and encoded in base64.
   9. Output is sent back to WhatsApp for display to user.
   
   This design promotes security, modularity, and adaptability. It's ideal for scenarios where multiple input types and screen states dictate different types of responses—all within the secure messaging platform WhatsApp.
   
   🛠 No Third-Party APIs? No Problem
   
   Interestingly, this entire workflow interacts only with WhatsApp’s encrypted payload—there are no calls to third-party APIs. The heavy lifting is done internally using custom JavaScript in n8n’s code nodes, showcasing how versatile this platform can be for secure, dynamic chatbot flows.
   
   Conclusion
   
   If you’re managing complex conversational flows over WhatsApp—like appointment bookings, surveys, or queue systems—this n8n workflow provides a highly secure, low-code way to handle it all. With hybrid encryption, modular routing, and dynamic response generation, you're set to offer users a seamless and protected experience with every click.
   
   Now that's smart automation, secured.
   
   —
   
   Want to try something similar in your chatbot stack? Fork and customize this workflow to fit appointments, seat reservations, or any other secure form-based interaction!

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.