Trigger-Based Workflow Automation

How we built AI agents that run a LOT of our business using n8n (detailed breakdown) After a few months of refining our automation stack at Kendo ai and my other companies, I want to share exactly how we're using n8n to create AI-powered workflows that operate 24/7 First, what is n8n? It's an open-source workflow automation tool that connects your apps, data, and APIs. Think Zapier but far more powerful and customizable. Here's our exact setup for creating autonomous agents: The foundation: Customer data flow Every customer interaction feeds into our CRM n8n monitors for trigger events (new signup, support request, etc.) Custom nodes extract relevant context from these events Real example: When someone books a demo, our n8n workflow instantly creates a personalized onboarding sequence based on their company size, industry, and the specific features they showed interest in. The intelligence layer: AI integration n8n connects to Claude API and GPT-4 for different reasoning tasks We've built custom prompts for each business function Webhooks allow the AI to trigger specific responses Real example: Our support system has n8n workflows that analyze incoming tickets, categorize the issue, retrieve relevant documentation, and draft personalized responses for our team to review before sending. The action layer: Automated responses Sentiment analysis determines appropriate response paths n8n routes information to the right team member Custom logic handles exceptions and escalations The continuous improvement cycle: Every interaction gets logged and analyzed n8n pulls data weekly to identify bottlenecks Successful patterns get reinforced in updated workflows The game-changer has been creating specialized agents for different functions: Content Agent: Monitors our post performance, suggests topics based on search trends, drafts outlines, and schedules publication Sales Agent: Uses customer info to help create the ai prospects inside of Kendo Ai. Basically creating randomized life stories for the Ai Data Agent: Pulls insights from customer behavior, generates reports, and flags opportunities we're missing Advanced techniques we're using: Chaining multiple n8n workflows together for complex decision trees Using temporary data storage to maintain context between workflow runs Creating feedback loops where one agent can trigger another Start with a single, high-value process and perfect it before expanding. Our first workflow just handled meeting scheduling, but it was the foundation for everything else. (Pro tip: Start with the cloud version to learn, then migrate to self-hosted for more control and to avoid workflow execution limits) N8N is like zapier on steroids and while it has a bit of a learning curve, it is well worth the time investment and automating things inside of your business Comment "n8n" below and i'll try my best to send you all some of the templates we use 🤝🏽 #n8n #ai

Did you ever need to have a Power Automate flow trigger on a new/updated item in one table, but only when certain conditions are met in a related table? I've been asked about this during my session at the #NordicSummit recently. And I've needed it myself in the past, too. So, imagine that you need to process new tasks when they appear under a project, but only if the project is active (let's say, identified via a Boolean field or a Date field on the Projects table). Or, in my case, I had Projects and Submissions, where the Projects table had a Category field which was a global choice. And since submissions under two projects needed to be processed automatically when they appeared, but in different ways, I wanted to build separate flows, that would trigger on new submissions but only for the relevant project. My case was slightly easier, because I would still need to fire the trigger on every submission, and could just split the processing logic across child flows. But there are definitely scenarios where we would not even want the trigger to fire at all if the conditions on the related table are not met. However, there is no way to expand the trigger conditions to related tables natively, as values from related tables are not a part of the trigger outputs in Power Automate. So, the seemingly only option would be to have the flows fire too frequently and then have a condition in the very beginning to terminate the flow if it is irrelevant based on the related table. Not very efficient, if you ask me. So, a possible solution to that could be adding a calculated field to the target table that would fetch a value from the related table. We used to do that previously quite a bit. But when I tried doing it now, it said that Calculated columns are being deprecated and we should use a new type called Formula now. Funnily enough, the info on "Formula" tables states that it allows making calculations based on the fields *within the same table*, which is a bit misleading. I thought this was a limitation and I will no longer be able to fetch data from related tables this way. However, it actually works perfectly fine and the syntax is so simple, I'm more than happy to stop using Calculated columns now. The limitation, obviously, is that it needs a N:1 relationship where the target table has a lookup to the related table. When we have that, we can simply use {RelatedTableName}.{ColumnNameInRelatedTable}. And it comes back with suggestions and auto-fill, so it really is extremely easy to use. May not work in all scenarios if you need those conditional triggers on tables you cannot edit, but if you can, this could really save you lots of work and lots of irrelevant flow runs.

I’ve been experimenting with ways to bring AI into the everyday work of telco — not as an abstract idea, but as something our teams and customers can use. On a recent build, I created a live chat agent I put together in about 30 minutes using n8n, the open-source workflow automation tool. No code, no complex dev cycle — just practical integration. The result is an agent that handles real-time queries, pulls live data, and remembers context across conversations. We’ve already embedded it into our support ecosystem, and it’s cut tickets by almost 30% in early trials. Here’s how I approached it: Step 1: Environment I used n8n Cloud for simplicity (self-hosting via Docker or npm is also an option). Make sure you have API keys handy for a chat model — OpenAI’s GPT-4o-mini, Google Gemini, or even Grok if you want xAI flair. Step 2: Workflow In n8n, I created a new workflow. Think of it as a flowchart — each “node” is a building block. Step 3: Chat Trigger Added the Chat Trigger node to listen for incoming messages. At first, I kept it local for testing, but you can later expose it via webhook to deploy publicly. Step 4: AI Agent Connected the trigger to an AI Agent node. Here you can customise prompts — for example: “You are a helpful support agent for ViewQwest, specialising in broadband queries – always reply professionally and empathetically.” Step 5: Model Integration Attached a Chat Model node, plugged in API credentials, and tuned settings like temperature and max tokens. This is where the “human-like” responses start to come alive. Step 6: Memory Added a Window Buffer Memory node to keep track of context across 5–10 messages. Enough to remember a customer’s earlier question about plan upgrades, without driving up costs. Step 7: Tools Integrated extras like SerpAPI for live web searches, a calculator for bill estimates, and even CRM access (e.g., Postgres). The AI Agent decides when to use them depending on the query. Step 8: Deploy Tested with the built-in chat window (“What’s the best fiber plan for gaming?”). Debugged in the logs, then activated and shared the public URL. From there, embedding in a website, Slack, or WhatsApp is just another node away. The result is a responsive, contextual AI chat agent that scales effortlessly — and it didn’t take a dev team to get there. Tools like n8n are lowering the barrier to AI adoption, making it accessible for anyone willing to experiment. If you’re building in this space—what’s your go-to AI tool right now?

I’ve been thinking a lot about how much time we waste just getting a website started. Not the design polish. Not the copy. Just… the setup: templates, builders, hosting, and endless tweaking before you even know if the idea is worth it. So I asked myself a simple question: What if a website could start the same way an AI chat starts… with one prompt? I ended up building an AI-powered workflow in n8n that generates and deploys a complete website automatically. Here’s the flow: → Chat trigger captures the website description → AI Agent turns it into a clean website brief → Google Gemini generates a full HTML/CSS file → GitHub Pages publishes it live in seconds And the wild part? It’s not a “demo” that stops at a mockup. It actually ships a live site. The first time I watched it deploy a full website in seconds from a single sentence… I realized this isn’t just “cool automation.” This is a new way to prototype. Because now: • Founders can validate ideas faster • Designers can get instant mockups • You can skip monthly website builder fees • Agencies can scale delivery with repeatable automation I break down the entire build step-by-step in my latest tutorial and you can download the workflow template for free when you join my Skool community here - https://lnkd.in/gtAExXGv If you’re experimenting with AI automation, this is one of the best “start here” projects. Drop a “WEBSITE” in the comments and I’ll send you the link to the full tutorial.

Record-Triggered Flows are powerful, but choosing the wrong type can create unnecessary DML, slower performance, and automation conflicts. Here’s a clean, technical breakdown of when to use each: 1️⃣ Before-Save Flow (Fast Field Updates) Runs before the record is saved to the database. This is Salesforce’s fastest automation. Use it for: Updating fields on the same record Simple formulas or assignments Lightweight validations Replacing most before-triggers Benefits: No DML required Up to 10x faster than After-Save Ideal for high-volume updates Avoid when: You need to create related records You need to make callouts You depend on record IDs after insert 2️⃣ After-Save Flow (Post-Commit Logic) Runs after the record is saved to the database. Use it for: Creating related records Updating other objects Sending emails Making callouts Publishing Platform Events Complex business logic Benefits: Full record values available ID is generated for new records Supports DML & downstream automation Avoid when: You only need field updates Logic runs too frequently It causes recursion or performance issues Quick Rule of Thumb If you only update the same record → Before-Save If you need anything else → After-Save Final Thought: Choosing the correct Flow type isn’t about preference — it’s about performance and design clarity. #Salesforce #FlowBuilder #SalesforceDeveloper #HRTech #Hiring #UsJob

🎁 ANOTHER UPDATE: Webhooks are now officially inside our workflow tool as a Trigger. Meaning you can now send data in & out of Trigify.io. This means you can now programmatically run your entire Social operations from us. No clicking. Just data flowing between your apps. =================== I’m just skimming the surface, but here are the first 2 workflows I built (that you can steal): 1. The "Auto-Clean" CRM (Attio + Trigify.io) I hate stale data. So I set up a workflow that runs every month: Trigger: Find any contact in Attio not updated in 12+ months. Action: Send via Webhook to Trigify. Enrich: We check for job changes. Result: If they moved, we create a signal and update the CRM automatically. If not, it stays put. 2. The Engagement Miner (Clay + Trigify) This connects our data to Clay tables instantly. Trigger: Input a list of post URLs into Clay. Action: Webhook sends them to Trigify. Enrich: We scrape the likes, enrich the profiles, and send the data back into Clay. Result: A fully enriched list of high-intent leads, completely on autopilot. =================== The long-term vision? You should be able to programmatically set up your entire Trigify account without ever logging in.