This article provides a complete, practical walkthrough of the Manual Http 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 Product Data Enrichment with AI: A No-Code Workflow Using n8n, OpenAI, and Airtable
   
   Meta Description:  
   Discover how to automate product data enrichment using Airtable, n8n, OpenAI vision models, and third-party APIs like SERP API and Firecrawl. Save hours of manual labor with an AI-powered workflow.
   
   Keywords:  
   n8n workflow automation, OpenAI vision model, Airtable automation, product enrichment, reverse image search, Firecrawl API, SERP API, AI agent, no-code AI integration
   
   Third-Party APIs Used:
   
   - OpenAI API – for image analysis (vision model) and AI Assistant logic
   - Airtable API – for reading from and writing to a product survey database
   - SerpAPI – for performing reverse image searches using Google's engine
   - Firecrawl API – for scraping and retrieving webpage content in markdown format
   
   —
   
   Article:
   
   Automating Product Attribute Enrichment with AI and n8n
   
   In today’s hyper-automated world, data collection and enrichment are key challenges for businesses that handle physical inventories or conduct field surveys. Whether you're tracking office equipment, furniture, or building assets, the manual process of labeling, describing, and logging product information can consume hours of valuable time.
   
   That's where low-code and no-code automation tools like n8n come in. This tutorial explores how to use n8n, OpenAI's powerful vision and language models, and external APIs to fully automate a visual product classification and enrichment workflow. The end result? A smarter, hands-free solution that turns images into structured product metadata.
   
   Let’s break down this end-to-end AI-powered automation workflow.
   
   Step 1: Capturing Data with Airtable
   
   The foundation of this process is a database that stores photos and basic metadata about products. This is handled using Airtable, selected for its API-friendly interface and flexible structure.
   
   Rows in a survey table contain product images and an AI_status field that flags whether a photo has already been processed. The workflow begins by pulling all rows where:
   
   - The product has an image
   - The AI_status flag is false
   
   This ensures the automation runs only on new items that haven’t yet been enriched.
   
   Step 2: Analyzing Images with OpenAI's Vision Model
   
   Once a product row is retrieved, the next step is to analyze the image using OpenAI's multi-modal vision model (via gpt-4o). The workflow does the following:
   
   - Focuses on the product in the image
   - Attempts to extract six core attributes:
   
     - Title
     - Description
     - Model
     - Material
     - Color
     - Condition (rated as Poor, Good, Excellent)
   
   This step provides an intelligent first-pass analysis, perfect for cases where visual cues alone are enough to describe the item.
   
   Step 3: AI Agent for Internet Research
   
   Let’s say the image analysis can’t determine all product attributes accurately. The solution? An embedded AI agent powered by OpenAI that mimics how a human might solve this challenge—by researching the web.
   
   The AI agent is supplied with:
   
   - The original product image
   - The results of the image analysis
   
   It can then invoke two tools:
   
   - Reverse Image Search Tool (using SERP API)
   - Web Scraping Tool (using Firecrawl API)
   
   The purpose: to match the photographed item against online records, retrieve relevant pages, and extract additional descriptive information. Parsing and decision-making happen via a structured output parser—ensuring consistent results.
   
   Step 4: Custom Tool Invocation via Workflow Tools
   
   An elegant aspect of the architecture is its use of dynamically-routed sub-workflows, making each tool behave like a modular plugin. Each tool (reverse image or web scrape) is called through a centralized router that reads a “route” parameter and sends the query to the appropriate endpoint.
   
   This scalable design allows additional tools to be added with minimal changes to the core workflow—making your AI agent smarter as needed.
   
   Step 5: Enriching Airtable Rows
   
   Once all attribute data is collected—either from AI vision analysis or external research—it is written back to the Airtable row.
   
   This includes fields like:
   
   - Title
   - Description
   - Model
   - Material
   - Color
   - Condition
   - AI_status (set to true)
   
   This marks the row as complete, preventing repeat processing.
   
   Benefits & Use Cases
   
   This kind of automation is a game-changer for any organization dealing with objects that need classification or labeling based on visual input. Common applications include:
   
   - Building surveys
   - Inventory management
   - Marketplace listings (e.g., secondhand goods)
   - Insurance claims assessment
   - eCommerce cataloging
   
   By leveraging the strengths of multiple APIs and AI tools, you dramatically reduce human effort while boosting accuracy.
   
   Conclusion: Low-Code, High Impact AI
   
   The n8n-based automation showcases how to combine image recognition, web scraping, and dynamic AI agents using a no-code approach. It saves hours of manual labor and delivers structured, high-quality data ready for reporting or further analysis.
   
   If you’re looking to scale operations without scaling your team, this AI-enriched system is a prime example of what's now possible through creative tool chaining and intelligence-in-the-loop design.
   
   Try it out, customize it, and unlock the next level of productivity in your data pipeline.
   
   —
   
   Want help deploying this workflow? Join the n8n community on Discord or explore more AI automation templates at docs.n8n.io.
   
   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.