This article provides a complete, practical walkthrough of the Filter Manual Send Triggered 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:
   Automate Outlook Email Categorization with AI Using n8n: A Step-by-Step Workflow Breakdown
   
   Meta Description:
   Learn how to automatically categorize and organize Outlook emails using AI and the no-code tool n8n. We explore a complete workflow that leverages language models, logic filters, and Microsoft Outlook for seamless inbox management.
   
   Keywords:
   Outlook email automation, n8n workflow, AI email categorization, email sorting automation, Microsoft Outlook integration, LangChain n8n, Ollama chat model, no-code AI email assistant, email productivity tools, inbox zero automation
   
   Third-Party APIs Used:
   
   - Microsoft Outlook API (via n8n Outlook Nodes)
   - LangChain AI Agent (via n8n LangChain integration)
   - Ollama Language Model (Qwen2.5:14b)
   
   —
   
   Article:
   
   Effortless Email Organization: How to Auto-Categorize Outlook Emails Using AI and n8n
   
   Inbox overload is one of the biggest productivity drains of the digital age. Whether you're freelancing, managing a team, or juggling multiple projects, hunting through hundreds of emails to find what matters can be exhausting. Luckily, automation tools like n8n and AI models such as Ollama make email triage easier than ever.
   
   In this article, we break down a powerful n8n workflow built by Wayne Simpson of nocodecreative.io that automatically scans, categorizes, and sorts incoming Outlook emails with the help of artificial intelligence. The result? A smarter inbox that organizes itself.
   
   🎯 Goal: Automatically read uncategorized Outlook emails, use AI to assign a category, update the email metadata, and move it to the appropriate folder.
   
   Let’s dig into how this works.
   
   📥 Step 1: Retrieve Unprocessed Outlook Emails
   
   The workflow begins with a trigger node called “When clicking ‘Test workflow’,” ideal for testing or manually kicking off execution. The connected Microsoft Outlook node queries the inbox for emails that are:
   
   - Not flagged
   - Have no categories already assigned
   
   This ensures that any already processed or manually submitted items are left untouched.
   
   📤 Step 2: Filter & Batch Process Emails
   
   Using a Filter and Split in Batches node, the workflow narrows down the email flow to a manageable set of items that meet our criteria. This allows the rest of the workflow to loop through emails one at a time for individual processing.
   
   🧼 Step 3: Sanitize Email Content
   
   Before feeding emails to an AI model, their content is cleaned and normalized using a Markdown node and a Set node. This step strips out HTML tags, removes unnecessary Markdown links and images, and gets rid of odd characters and long whitespace—ensuring only relevant readable content remains for analysis.
   
   🧠 Step 4: Categorize Using AI Agent
   
   Here’s where it gets exciting.
   
   A LangChain AI Agent node (powered by the Ollama Qwen 2.5 14B language model) is invoked to analyze each email. It receives structured information like subject, sender, and cleaned body content within a defined XML-style markup.
   
   The agent follows a strict prompt that outlines valid categories:
   
   - Action
   - Junk
   - Receipt
   - SaaS
   - Community
   - Business
   - Other
   
   Subcategories can also be assigned, such as “action” as a subcategory of “SaaS”.
   
   The AI’s response is enforced to be in strict JSON format, ensuring the next automation steps can reliably consume it.
   
   💾 Step 5: Parse & Validate the JSON Output
   
   Once the AI returns its classification, a Set node parses and extracts the JSON object from the output using regex-based extraction. Even if the AI model adds unexpected intro text or commentary, the workflow handles it gracefully.
   
   If any parsing errors occur, an error handler catches them and allows the workflow to continue.
   
   📂 Step 6: Switch & Set Categories in Outlook
   
   At this point, the workflow evaluates which category was returned using a Switch node. Depending on the result (e.g., “junk” or “receipt”), the workflow proceeds to one of several Microsoft Outlook nodes that apply the email’s new category metadata.
   
   Outlook’s "update" API operation sets categories like “Junk” or “Business” directly into the email record.
   
   🗂 Step 7: Move Email to the Right Folder
   
   Each category also has a corresponding folder, such as “Community”, “SaaS”, or “Actioned”. A new set of Outlook nodes move the email into the appropriate folder, effectively cleaning the inbox while also segmenting all future email types.
   
   ✅ Step 8: Read Status Logic
   
   Before concluding, the workflow checks if the email has been marked as “read”. If it has, it gets moved directly to the “Actioned” folder. If not, it stays in its new category folder, awaiting human review.
   
   This smart branch ensures that processed-but-still-unread emails are treated separately from those you’ve already skimmed.
   
   🧩 Modular, Extendable Design
   
   Sticky notes throughout the workflow provide guidance on customization—e.g., how to change categories, update AI prompts, or adjust sanitization routines.
   
   The system is not just functional, but also thoughtfully documented and made accessible for those wishing to customize their flows without diving deep into code.
   
   🔌 Technologies Used
   
   The brilliance of this workflow lies in its use of complementary technologies:
   
   - Microsoft Outlook Node (retrieves, categorizes, moves messages)
   - LangChain’s AI Agent Node (categorizes emails with advanced understanding)
   - Ollama’s Qwen 2.5 Large Language Model (provides reliable, low-temperature outputs)
   - n8n Workflow Logic (SplitInBatches, Switch, Filters, Try-Catch, and Merges)
   
   Together, they create a seamless AI-powered inbox assistant with automation precision.
   
   🧠 Final Thoughts
   
   Inbox management doesn't have to be human-only. With powerful tools like n8n and AI integrations, you can automate everyday email sorting, reducing noise and focusing on what really matters.
   
   This workflow, created by nocode developer Wayne Simpson, is a masterclass in AI and workflow orchestration—and a great foundation for anyone looking to take control of their inbox, one category at a time.
   
   📽 Watch the setup video here »  
   ▶️ https://www.youtube.com/watch?v=EhRBkkjv_3c
   
   —
   
   Want to try it yourself? Check out Wayne’s work on nocodecreative.io and start automating today.

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.