Voice AI agents have come a long way. What used to take a bunch of custom tools and webhook wiring can now be done much faster using MCP. In this video, I show how to build a simple AI receptionist that: - Answers calls - Collects customer info - Checks availability - Books appointments into Google Calendar - Saves the customer record into Google Sheets - Sends a confirmation email Here is the flow, step by step: 1. Set up the voice agent in Vapi - Use GPT 4.1 mini for lower cost and faster responses - Make the assistant speak first - Add a system prompt with identity, tone, and tool instructions 2. Connect Vapi to n8n using MCP - Add an MCP Server Trigger in n8n - Build your tools as sub workflows - In Vapi, create an MCP tool and paste the MCP server URL - Add an Authorization header using your n8n API key 3. Create the sub workflows (these become your tools) - Get user - Search the Google Sheet by email - If not found, return “new client” so the agent knows to create them - Create user - Append a new row with name, phone, email, and call date - Get available slots - Pull calendar events for the requested date - Return busy times between business hours (example 9:00 AM to 5:00 PM) - Book appointment - Create the Google Calendar event - Update the user row with service type and appointment time - Send a confirmation email 4. Let the model fill tool parameters automatically - In the MCP trigger inputs, use “defined automatically by the model” - This avoids extra agents and reduces latency and cost 5. Test the full experience end to end - Call the number - Book an appointment - Confirm the data shows up in Sheets and Calendar - Confirm the email is sent If you want to build this exact setup, I included the resources and workflows in the description. Subscribe for more tutorials on voice AI agents, MCP, and real automations you can deploy. https://youtu.be/IiTV70i-2zA?si=IQAZEllSWNTqoPyH

Google just released Gemini 3 and with it their new image model Nano Banana Pro. The results are crazy realistic and the price is surprisingly low. So I built a full image generation system in n8n that lets you create images through a simple chat interface. The bot can generate three types of images. You can create an image from text. You can use an existing image as a reference. Or you can upload multiple images and combine them into a brand new one. I walk through how the whole system works: 1. How to connect Nano Banana Pro through Kie.ai using HTTP requests 2. How to build a clean n8n form that collects prompts and file uploads 3. How to enhance prompts with AI before sending them to the model 4. How to use Google Drive as a free image host for the uploaded files 5. How to handle the async generation loop until the image is done 6. How to send the final image back through a Telegram chat The bot can make infographics, edits, memes or realistic image composites. And you can run the whole thing from your phone. If you want more builds like this, make sure you subscribe. https://youtu.be/sJsHQ3J1erI?si=8KcVFwNno7R3gFk0

If you are building AI agents in n8n, there are a few features that can take them from basic to genuinely useful. A lot of people only use the default setup, and they miss out on things that make agents more reliable, more accurate and much more powerful. In this video I break down five features that every agent should be using. Some of them are simple. Others are overlooked because most tutorials skip them. I walk through everything using a real example, which is my budget tracking agent that logs expenses into a spreadsheet through Telegram. Here is what I cover: 1. How to use a system prompt the right way 2. How tool calling works and how to let the agent fill parameters on its own 3. How to use structured output so your agent returns clean and predictable data 4. How simple memory works and when to store chat history 5. How to delegate tasks to a sub agent and avoid long messy prompts These features apply to support agents, CRM update bots, appointment setters and any workflow that relies on AI reasoning. If you want more agent breakdowns and practical builds, make sure you subscribe.

Carousels have taken over LinkedIn and Instagram. They are one of the easiest ways to share tips, tool stacks and educational content in a format people actually stop to read. I built a simple system that creates the full carousel content for you using any YouTube video as the source. You just paste the video URL, add optional instructions and the workflow generates every slide, the title, the bullets, the image ideas and even the caption for the post. This lets you turn long videos into clean, high value carousels without writing everything from scratch. All you do after is drop the content into a Canva template and upload it to LinkedIn as a document. In the video I walk through: 1. How to pull a full video transcript automatically 2. How to run it through AI to create the slide content 3. How to structure titles, bullets and image placeholders 4. How to generate a strong caption for the post 5. How to store everything in a simple data table If you create content or you want to post more consistently, this workflow saves a ton of time and removes the hardest part which is coming up with the actual slides. https://youtu.be/E8BZHZJpvsc?si=sbNVPxVca2xDG9M_

I made a full walkthrough on how to build a RAG based support agent using n8n, Firecrawl, Supabase and OpenAI. Sharing it here because RAG sounds complex, but the setup is actually pretty simple once you see the steps. Here’s the quick breakdown of the workflow: 1. Scrape the website with Firecrawl It grabs every page and formats everything into clean markdown. 2. Upload the files to Drive Just to store all the scraped content in one place. 3. Pull the files into n8n Download each file so n8n can process them. 4. Convert markdown into text Supabase needs plain text before embedding. 5. Create a Supabase vector store Run one SQL snippet and your table is ready. 6. Embed and upload n8n chunks the text, embeds it and sends it to Supabase. 7. Connect a Telegram bot This becomes the front end for talking to the agent. 8. Build the agent The agent pulls relevant info from Supabase and answers based on what’s actually in your data. The video walks through every step if you want to build one yourself. If you want more step by step tutorials like this, please subscribe to my channel. I will be posting more in the upcoming weeks :)