Automating Email Responses Without Losing Context

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!

Recently, a client reached out to us expressing frustration with the RAG (Retrieval-Augmented Generation) application they had implemented for customer support emails by a different AI agency. Despite high hopes of increased efficiency, they were facing some significant problems: The RAG model frequently provided wrong answers by pulling information from the wrong types of emails. For example, it would respond to a refund request email with details about changing an order - simply because those emails contained some similar wording. Instead of properly classifying the emails by type and intent, it seemed to just perform a broad embedding search across all emails. This created a confusing mess where customers were receiving completely irrelevant and nonsensical responses to their inquiries. Rather than streamlining operations, the RAG implementation was actually making customer service much worse and more time-consuming for agents. The client's team had tried tuning the model parameters and changing the training data, but couldn't get the RAG application to accurately distinguish between different contexts and email types. They asked us to take a look and help get their system operating reliably. After analyzing their setup, we identified a few key issues that were derailing the RAG performance: Lack of dedicated email type classification The RAG model needed an initial step to explicitly classify the email into categories like refund, order change, technical support, etc. This intent signal could then better focus the retrieval and generation steps. Noisy, inconsistent training data The client's original training set contained a mix of incomplete email threads, mislabeled samples, and inconsistent formats. This made it very difficult for the model to learn canonical patterns. Retrieval without context filtering The retrieval stage wasn't incorporating any context about the classified email type to filter and rank relevant information sources. It simply did a broad embedding search. To address these problems, we took the following steps with the client: Implemented a new hierarchical classification model to categorize emails before passing them to the RAG pipeline Cleaned and expanded the training data based on properly labeled, coherent email conversations Added filtered retrieval based on the email type classification signal Performed further finetuning rounds with the augmented training set After deploying this updated system, we saw an immediate improvement in the RAG application's response quality and relevance. Customers finally started getting on-point information addressing their specific requests and issues. The client's support team also reported a significant boost in productivity. With accurate, contextual draft responses provided by the RAG model, they could better focus on personalizing and clarifying the text - not starting responses completely from scratch.

AI handles the admin, humans make the hires. Our agent-lite workflow keeps HITL at the core We built an AI agent (lite) to respond to admin related candidate questions using n8n + GPT-4. When candidates respond to outreach or ask about a role, it: 1. Picks up the inbound email via Gmail 2. Finds the relevant job spec from Google Drive 3. Extracts the candidate’s question 4.vPasses it to GPT-4 to generate a natural, 250-character reply 5. Creates draft in gmail with a Calendly link built in Why we built it: To reduce manual back-and-forth. Why “agent-lite”? It’s more than just an automation — it reads unstructured questions, uses job context to decide what matters, and writes human-like replies. But it still runs on a fixed flow — not fully autonomous. So: intelligent, but scoped. What tripped me up: - GPT-4 was too verbose until we tightened the prompt - Google Drive search returned duplicate specs — had to filter - Formatting of email still needs work - Gmail draft replies broke until we passed the exact message ID It’s now live and rolled out. Helping our team stay focused on people, the agent handles the admin How are you deploying Ai in your workflow?

How I built an AI email assistant that organizes, triages, and drafts replies. (without losing my brand voice). Last Friday, we ran a session on designing and building an AI agent for inbox management. Here’s what we covered: (and how you can follow the same steps): Step 1: Map your current process. Before you build anything, understand what you're already doing. → How do you currently handle email? → Where do things fall through the cracks? → What decisions do you make over and over? Most founders skip this step. But if you automate a broken system, you create chaos faster. Step 2: Fill out the Agent Canvas. We used our 5-part framework to map the full logic of the system: 1 - Triggers: What sets the process in motion? (e.g., new email, daily schedule) 2 - Decisions: What logic drives next steps? (e.g., is this urgent?) 3 - Actions: What should the agent do? (e.g., apply labels, draft reply) 4 - Tools: What platforms does it need? (e.g., Gmail, Slack, Claude) 5 - Guardrails: Where do humans stay in control? (e.g. drafts only, escalate via Slack) Step 3: Build your agent using natural language. Once the canvas was mapped, we used Lindy’s builder to create a real working agent (no code required). Example: → An assistant that runs 3x/day. → Checks for priority senders. → Applies labels. → Pulls answers from the knowledge base. → Drafts replies. → Pings Slack for anything urgent. No pre-built workflows. Just clear logic, explained in plain English. Step 4: Iterate. Most builds won’t work perfectly on the first try. That’s part of the process. We shared broken versions in the community, refined the templates, and got live feedback. The takeaway? You don’t need AI to answer everything. You need a system that understands how you triage, reply, and escalate. Then builds around that. And that’s exactly what we help founders do inside the Mighty AI Lab. Ready to build an AI email assistant? Join the Lab: https://lnkd.in/gjah4Yen