This article provides a complete, practical walkthrough of the Code 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 Expert-Level Knowledge Base Creation with n8n and AI Agents
   
   Meta Description: Discover how to fully automate the creation, review, and publishing of research-grade knowledge base articles using n8n, Perplexity AI, OpenAI, and Contentful. Learn how AI agents collaborate to ensure article quality and consistency.
   
   Keywords: n8n automation, knowledge base generator, Perplexity AI, OpenAI GPT, AI article writer, Contentful integration, AI editorial workflow, recursive content refinement, LLM-driven publishing
   
   —
   
   Short Article (850 words):
   
   Automating High-Quality Knowledge Base Articles with n8n and AI Assistants
   
   In today's fast-paced digital landscape, creating consistent, high-value knowledge base content at scale is a major challenge for businesses, educators, and content managers. But what if every knowledge base article could be automatically researched, written, edited, and published—without compromising on quality?
   
   Enter Auto Knowledge Base Article Generator: a robust, AI-powered automation workflow built in n8n that leverages LangChain agents, Perplexity AI's deep research models, OpenAI's GPT family, and seamless publishing via Contentful. This workflow orchestrates a complete production pipeline—think writer’s room meets editorial board—all powered by intelligent agents.
   
   Let’s walk through how this powerful automation works, and where AI-driven creativity meets stringent editorial standards.
   
   The Workflow Overview
   
   At its core, the system accepts a chat prompt or topic query and turns it into a multi-stage pipeline to automatically draft, edit, improve, and publish full-length knowledge base articles.
   
   The pipeline is composed of five key stages:
   
   1. Content Writing via AI Writer Agent
   2. Enhanced Research with Perplexity AI
   3. Editorial Review and Feedback Loop
   4. Quality Assurance with Iteration Limits
   5. Final Publishing to Contentful CMS
   
   Let’s explore each of these in detail.
   
   1. AI Writer Agent: Structured Content Creation
   
   It all starts with the user submitting a topic or chat prompt—either through a direct webhook or chatbot interface. The AI Writer Agent (powered by OpenAI and LangChain) accepts this request and constructs an article with a predefined, contentful-friendly JSON schema.
   
   This includes fields like:
   - Title & slug
   - Category assignment (e.g., "getting-started", "career")
   - Keywords
   - Body content (markdown compatible)
   - Meta tags (metaTitle, metaDescription)
   - Reading time estimation
   - Difficulty level
   
   If the system is updating an existing article, the agent carefully injects improvements only into the content field—preserving the original intent and title.
   
   2. Deep Research with Perplexity AI
   
   To support long-form, research-backed writing, the workflow invokes Perplexity AI’s /chat/completions endpoint using their sonar-deep-research model. The AI is given the article title and requested to expand upon it with:
   - Essential subtopics
   - Suggested resources
   - Best practices
   - Citations
   
   It’s instructed to return content in rich markdown style, formatted with headers, tables, and emphasis (bold and italics). The JSON response is parsed and cleaned, then citations are converted into markdown bullet-style links.
   
   This allows articles to balance opinionated writing with well-researched authority.
   
   3. Editor Agent: Quality Control with Clear Rules
   
   Once the research-backed draft is ready, an Editor Agent (also built with LangChain) conducts a quality inspection against critical benchmarks:
   - Clarity and usefulness
   - Completeness and specificity
   - Overall readability and flow
   
   The editor never alters the content—rather, it appends a field called “improvements” and sets an action: either "rewrite" (try again) or "submit" (good enough to publish).
   
   Here’s where things get smart: Each article is allowed up to 3 rounds of auto-revision before a "Stop Here" node halts further iterations to avoid indefinite loops.
   
   4. Iterative Refinement Loop
   
   If an article doesn’t pass review, a counter-based loop automatically triggers a new round of improvements using the AI Writer agent and Perplexity again, based on the editor's precise feedback.
   
   This cycle continues with updated revisions, and the counter ensures a max of 3 feedback cycles occurs. This way, the system walks a fine line between continuous improvement and processing efficiency.
   
   5. Accept and Publish via Contentful
   
   When the editor agent finally deems an article ready, the “Accept and Publish” node formats the content fields appropriately for Contentful's CMS, including Rich Text conversion.
   
   The system calls a subworkflow called Publish to Contentful, ensuring the structured article is published in real time to your blog, documentation, or public knowledge base.
   
   Bonus: AI model formatting can be trained for Contentful-specific fields—just mention it to the team behind this at christian@varritech.com.
   
   Smart Fail-Safes and Edge Case Handling
   
   The workflow is robust in handling failures:
   - Articles with too many improvement cycles get rejected early via a “Check Limit” node
   - JSON parsing safety layers are used at multiple points to prevent format errors
   - Every node that touches content enforces a strict formatting schema
   - Workflow retries can be invoked without losing iteration state
   
   This balances AI creativity with hardcoded publishing rules so your content isn’t just fast—it’s accurate, structured, and GDPR-safe.
   
   Why This Matters
   
   Tools like ChatGPT offer power, but lack workflow integration. n8n, with its composable automation, brings discipline and repeatability to AI operations.
   
   Using this approach, teams can:
   - Dramatically scale knowledge production
   - Maintain editorial consistency without manual review
   - Integrate diverse AI technologies into one seamless pipeline
   
   This is AI tooling at its finest—modular, collaborative, and trusted enough to be automated end-to-end.
   
   —
   
   Third-Party APIs Used
   
   - OpenAI (GPT-3.5 / GPT-4.5) via LangChain (API key secured)
   - Perplexity AI (Sonar Deep Research Model – https://api.perplexity.ai/chat/completions)
   - Contentful CMS (used via a dedicated Publish Workflow)
   
   Optional Enhancements
   
   Interested in formatting AI-generated content for Contentful's Rich Text natively? Ask the team behind this automation at christian@varritech.com for the advanced formatter module.
   
   —
   
   With the Auto Knowledge Base Article Generator, content once requiring days of human research and writing can now be produced, revised, and published in under an hour—automatically, intelligently, and at scale.
   
   Welcome to the future of smart documentation.

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.