LAST UPDATED: FEBRUARY 8, 2026
n8n's visual workflow builder and native AI nodes make it one of the fastest ways to ship AI agent automations — without writing a framework from scratch. Here are real workflows people are running in production, organized by use case.
n8n sits at a sweet spot between no-code simplicity and developer flexibility. The visual builder lets you drag and connect nodes — triggers, AI models, API calls, data transformations — without writing boilerplate. But when the visual nodes aren't enough, you can drop in JavaScript or Python code inline without leaving the editor. This hybrid approach means you can prototype a workflow in an afternoon and harden it for production the same week.
The self-hosting option is what draws teams with data sensitivity or cost constraints. Running n8n on your own infrastructure means your AI workflows — including every prompt, every API call, and every piece of data processed — stays on your servers. For teams in healthcare, finance, or legal that can't send data to third-party clouds, self-hosted n8n is often the only viable option for AI agent automation. And at scale, the economics matter: unlimited executions on self-hosted n8n vs. per-operation pricing on cloud platforms can mean a 60-80% cost reduction for high-volume workflows.
Email workflows are the most common entry point for n8n AI automations — high volume, clear structure, and immediate time savings.
Monitors an inbox via IMAP or Gmail trigger. Each incoming email passes through an AI classification node that categorizes by urgency, topic, and required action. High-priority emails get forwarded immediately with a suggested response draft. Routine emails get auto-labeled and batched for daily review. Spam and irrelevant messages get archived automatically. Reduces inbox processing time by 70-80% for teams handling 100+ emails daily.
Key nodes: Gmail Trigger → AI Agent (classify) → Switch → Gmail (label/forward/archive)
Setup time: 1–2 hours
When a customer email arrives, the workflow pulls context from your CRM (their account details, recent tickets, purchase history), feeds it to an LLM alongside the email content, and generates a personalized reply draft. The draft goes to a human for review before sending — keeping the human in the loop while eliminating the 10 minutes it takes to research context and compose each response manually.
Key nodes: Email Trigger → HTTP (CRM lookup) → AI Agent (draft) → Slack (review notification)
Setup time: 2–3 hours
Takes meeting recordings or transcripts (from Zoom, Google Meet, or Fireflies), extracts action items, decisions, and key discussion points using an AI node, then distributes a structured summary to attendees via email and creates follow-up tasks in your project management tool. Turns a 60-minute meeting into an organized, actionable document in under 2 minutes.
Key nodes: Webhook (transcript) → AI Agent (extract) → Notion/Asana (create tasks) → Gmail (distribute)
Setup time: 2–3 hours
AI-powered sales and marketing automations are the second most popular category — high ROI and clear metrics make them easy to justify.
New leads from forms, ads, or inbound email trigger a workflow that enriches the lead data (company size, industry, job title via Clearbit or Apollo), scores them using an AI node against your ideal customer profile, and routes them to the right sales rep based on territory, deal size, or product interest. Hot leads get a Slack ping and immediate CRM entry. Cold leads enter a nurture sequence. Replaces manual lead qualification that typically takes 15-20 minutes per lead.
Key nodes: Webhook (form) → HTTP (enrich) → AI Agent (score) → Switch → HubSpot + Slack
Setup time: 3–4 hours
Takes a long-form piece of content — blog post, podcast transcript, or webinar recording — and produces platform-specific derivatives. An AI node generates a Twitter/X thread, a LinkedIn post, an email newsletter section, and a set of pull quotes, each adapted for its platform's conventions and character limits. One piece of content becomes five distribution assets in under 3 minutes.
Key nodes: Webhook (content URL) → HTTP (fetch) → AI Agent (repurpose ×4) → Airtable (store drafts)
Setup time: 2–3 hours
Periodically scrapes or fetches reviews from G2, Capterra, Trustpilot, and social media mentions. An AI node classifies each review by sentiment, extracts specific feature mentions and complaints, and flags negative reviews above a severity threshold. The workflow posts a daily digest to a Slack channel and creates tickets in your support system for reviews that need direct response. Turns scattered feedback into structured intelligence.
Key nodes: Schedule Trigger → HTTP (fetch reviews) → AI Agent (analyze) → Slack + Linear/Jira
Setup time: 3–4 hours
Support workflows benefit enormously from AI automation — high volume, repetitive patterns, and measurable outcomes.
Incoming support tickets (from email, chat, or forms) pass through an AI classification node that identifies the issue type, product area, urgency, and customer sentiment. The workflow automatically assigns the ticket to the right team, sets priority, and attaches relevant knowledge base articles. For common questions with confident matches, it drafts a response for one-click approval. Reduces first-response time from hours to minutes.
Key nodes: Webhook (ticket) → AI Agent (classify) → Switch (route) → Zendesk/Intercom (assign + draft)
Setup time: 3–4 hours
Monitors resolved support tickets for patterns — when the same question gets asked more than a threshold number of times and the answer isn't in the knowledge base, the workflow generates a draft article from the best resolved ticket responses. A human reviews and publishes. Over time, this closes gaps in self-service documentation automatically, reducing repeat ticket volume by 15-25%.
Key nodes: Schedule → Supabase/DB (query tickets) → AI Agent (identify gaps + draft) → Notion (create draft)
Setup time: 4–5 hours
Analyzes open ticket conversations in real-time. An AI node evaluates each customer message for frustration signals, complexity indicators, and churn risk. When the score crosses a threshold, the workflow alerts a senior support rep, adds context about the customer's history and account value, and suggests de-escalation approaches. Catches at-risk customers before they escalate to social media or cancellation.
Key nodes: Webhook (new message) → HTTP (customer history) → AI Agent (risk score) → Slack (alert)
Setup time: 3–4 hours
n8n's ability to connect databases, APIs, and AI nodes in a single workflow makes it powerful for operational automation.
Watches a folder (Google Drive, Dropbox, or S3) for new documents. When a file arrives, the workflow extracts text (using OCR for images and scanned PDFs), runs it through an AI node that identifies document type and extracts structured data (invoice amounts, contract dates, names, addresses), and loads the results into a database or spreadsheet. Processes invoices, receipts, contracts, and forms without manual data entry.
Key nodes: File Trigger → Extract Text → AI Agent (classify + extract) → Google Sheets/DB (store)
Setup time: 3–5 hours
Runs on a schedule to check competitor pricing across their websites and marketplaces. An AI node compares current prices against your pricing, identifies significant changes, and generates a summary with recommended responses. The workflow stores historical pricing data for trend analysis and alerts your pricing team when competitors make moves that warrant a response.
Key nodes: Schedule → HTTP (scrape pages) → AI Agent (compare + analyze) → Slack + Airtable
Setup time: 3–4 hours
Pulls metrics from your data sources on a schedule — revenue, signups, API latency, error rates — and feeds them to an AI node that identifies anomalies based on historical patterns. Instead of setting manual threshold alerts, the AI learns what "normal" looks like for each metric and flags deviations with context about what might have caused the change. Posts a daily health report and instant alerts for critical anomalies.
Key nodes: Schedule → HTTP/DB (fetch metrics) → AI Agent (detect anomalies) → Slack + Email
Setup time: 4–6 hours
Start with a workflow that automates something you currently do manually at least once a day. Email triage, lead classification, or content summarization are proven starting points because they're high-frequency, low-risk, and produce immediately visible time savings. Use the AI Agent node with a clear system prompt that defines the task, expected output format, and any constraints. Test with 20-30 real examples before connecting it to live systems.
The most common mistake is building too much automation at once. Start with a single trigger, a single AI node, and a single output. Get that working reliably, then add branches, error handling, and additional nodes incrementally. The n8n vs Make comparison can help you decide if n8n is the right platform for your specific use case, especially if you're evaluating no-code options.
Want to go deeper? The official n8n documentation includes a workflow templates library with ready-to-import AI workflows. The n8n community forum is also active with builders sharing workflows and troubleshooting tips.
n8n workflows act on behalf of users — sending emails, modifying data, creating records, making decisions. As these automations scale and handle more sensitive tasks, the people and systems on the receiving end need a way to verify which agent is acting and whether it's trustworthy.
RNWY provides identity infrastructure that works with any automation platform. Register the agents powering your n8n workflows, build transparent reputation through verified interactions, and give counterparties a way to check your agent's track record before engaging. The workflow handles the automation. The identity proves who's behind it.
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