Behind the scenes of auto-generated email replies

“𝘛𝘩𝘢𝘯𝘬𝘴 𝘧𝘰𝘳 𝘳𝘦𝘢𝘤𝘩𝘪𝘯𝘨 𝘰𝘶𝘵! 𝘞𝘦’𝘭𝘭 𝘨𝘦𝘵 𝘣𝘢𝘤𝘬 𝘵𝘰 𝘺𝘰𝘶 𝘢𝘴 𝘴𝘰𝘰𝘯 𝘢𝘴 𝘱𝘰𝘴𝘴𝘪𝘣𝘭𝘦.” That’s the sentence @Matthew killed first when he became VP of Support. He said: “If your first message doesn’t help the customer… it’s just noise.” So they replaced the auto-reply with something smarter. Now, before a human even sees the ticket, AI jumps in to: 🧠 Summarize the issue in plain English ⚠️ Flag urgency based on tone and keywords 🔁 Suggest the most likely next step It’s not trying to resolve the issue. It’s teeing it up—so the agent can. And that first message goes out within seconds. Not a deflection. Not a promise. A head start. Here’s what happened next: - Agents jumped in with real context - Customers stopped rewriting the same tickets twice - Resolution time went down, even with fewer people on shift Then they went one step further. If the AI doesn’t find enough context to summarize the issue, it automatically asks the customer for a screen recording—via Birdie. That way, agents get a short video of the issue, plus network and console logs, all in one go. Less guessing. Fewer follow-ups. More first-contact resolutions. Auto-replies are dead. Modern teams use AI to triage for humans, not replace them. If you're still using “𝘞𝘦’𝘭𝘭 𝘨𝘦𝘵 𝘣𝘢𝘤𝘬 𝘵𝘰 𝘺𝘰𝘶 𝘴𝘰𝘰𝘯,” You're wasting the most valuable message in the entire conversation. Anyone else rebuilding that first reply? I’d love to hear what you’re trying.

3 months ago, a client came to us with a simple complaint: “We’re losing deals because we reply too slow.” They were running outbound across email, LinkedIn, and inbound on website forms and were drowning in fragmented replies. No central inbox. No tagging. No way to prioritize what mattered. So we built them a system. Not a better workflow. A machine. Here’s how it works: 1. Everything goes into one inbox. Email, LinkedIn replies (HeyReach.io, Expandi , Aimfox we integrate them all). One dashboard, zero tab-hopping. 2. Every message gets sorted by AI. Responses are instantly categorized using custom prompts. Interested leads get tagged. Noise gets filtered out. 3. Draft replies are auto-generated. If someone’s interested, we send their details to a Clay table, generate a reply, and return it to Masterinbox.com automatically. 4.Slack lights up. An interested reply triggers a Slack notification. The thread includes a link to the master inbox. The draft reply is already sitting there ready to go. Just hit send. From cold reply to human like response in under 5 minutes. That client didn’t just close more deals. They changed how their team thinks about outbound. This isn’t sales automation. It’s sales augmentation. And when speed is your edge, 5 minutes is a moat.

Just built a 2-way intelligent email agent using n8n in ~10 mins, and recorded a step-by-step video for anyone looking to automate smart email workflows. 📌 Stack used:- 🔁 n8n (as the orchestrator) 🧠 OpenAI GPT model (for intelligent responses) 📬 Gmail (fetch + send + reply) 📊 Google Sheets (as a logging layer and intermediate state handler) 🧩 Workflow breakdown:- 🔘 Triggered via a manual button (can be scheduled or webhook-based) 📥 Pulls recent Gmail threads using getAll: message 📝 Feeds each email to OpenAI’s message model for response generation 📄 Logs both user email and GPT-generated reply into Google Sheets 📤 Sends the AI-generated response using Gmail (sendAndWait) 🔄 Monitors new replies using Sheets as state memory 📧 Sends a contextual follow-up using reply: message ⚙️ This is an early prototype of how AI + automation tools can transform email communication pipelines. 💡 Use cases:- 📞 Automated customer support 🎯 Lead engagement 🤖 Smart autoresponders 📂 Inbox triaging assistants If you are exploring LLM-powered agents, n8n automation, or building AI workflows with no-code tools - this is for you. Interested in learning more about AI agents? Dr. Raj Abhijit Dandekar (MIT PhD) is conducting a 10-day bootcamp on AI agents. See details here: https://lnkd.in/gn4aDWKW ***** 🔄 Feel free to reshare if this could help someone in your network! 👤 Follow me, Sreedath Panat, for more content on AI, ML, and automation workflows!

Most people associate “ticket deflection” with chatbots or Help Centers—but what about email? Traditionally, there was no real way to intercept or respond intelligently to incoming support emails… until now. Zendesk has quietly introduced a generative AI upgrade for email agents last week. With AI Agents Advanced, Zendesk AI agents (Ultimate) can now send custom replies to email tickets based on indexed Help Center articles or even your own website. No more static article links—these replies feel human, can be personalized in tone and format, and even include built-in fallback handling and tagging. I even set mine to delay replies slightly—because just like in The Founder, instant isn’t always believable. 🍔 This upgrade changes the game for email support. You can automate smart replies, reduce agent workload, and track AI impact through tags and status updates. Curious how to roll this out in your own instance? I detailed my setup—knowledge sources, personas, use case flows, and tips for humanizing your AI Agent—in this week’s Internal Note article. https://lnkd.in/exacz2_k