Ravi Kumar Blog Posts

If you're exploring agentic automation but don't want to start with something overwhelming, this is the project for you. In this post, I'll walk through how I built a simple Agent-to-Agent (A2A) pipeline using n8n and Google Gemini to automatically classify and respond to customer support tickets. The entire workflow is just three connected agents in a straight line — no parallel branches, no merge logic, no complicated routing. It's the perfect "hello world" for A2A automation, and you can have it running in under 30 minutes. The use case A customer submits a support ticket. Instead of a human reading it, tagging it, and drafting a reply, three AI agents handle the whole thing: A Classifier Agent reads the ticket and tags it as billing, technical, or general. A Response Agent drafts a reply based on that category and the original message. An Email Agent sends the reply straight back to the customer. Why this is a good starting project? Compared...

I experimented with n8n's AI Agent capabilities and built a simple yet powerful FAQ assistant without writing any custom code. What impressed me most is how quickly n8n allows us to move from a simple spreadsheet to a functional AI-powered assistant using an LLM, memory, and tool integrations—all through a visual workflow. This makes AI agent development accessible even for engineers who want to focus on business logic rather than infrastructure. The objective was straightforward: Store FAQs in Google Sheets Allow users to ask questions in natural language Let the AI Agent find the most relevant answer Maintain conversation context using memory Deliver responses through n8n's built-in chat interface Before working on the AI Agent flow design, pls make sure to have n8n community edition setup done. I have set it up in my Windows PC and here is the link to check on the setup. Link : https://pisupativenkatasesha.blogspot.com/2026/06/running-n8n-community-edition-on.html 🔹 Compon...

When it comes to workflow automation, one tool stands out for its unique blend of AI capabilities and business process automation: n8n. If you're learning AI and looking to explore n8n without breaking the bank, the n8n Community Edition is the perfect starting point. You can set it up locally on your own machine — making it an ideal environment for quick exploration and proof-of-concept (POC) projects. What is n8n? n8n (pronounced "n-eight-n," short for "nodes to nodes") is a visual process automation tool built on Node.js. It allows you to connect and monitor complex processes across a large number of services without needing to write boilerplate code. You simply drag, drop, and connect nodes — where each node represents an API call, data transformation, event trigger, or flow control element like loops and conditionals.  Key Features: Visual Workflow Builder — Build automation pipelines visually with a node-based canvas. No coding required for most use cases....

What we'll build: A local MCP server that connects Claude Desktop to the US National Weather Service API — giving Claude the ability to fetch live weather alerts and 3-day forecasts for any US location. Free, no API key needed. What is MCP? Model Context Protocol (MCP) is an open standard that lets Claude connect to external tools and data sources running on your machine. Think of it as a plugin system — you write a small server, register it with Claude Desktop, and Claude can call your tools mid-conversation as if they were built-in capabilities. In this post, we'll build a weather MCP server from scratch on Windows using Python and uv , and wire it directly into Claude Desktop. Prerequisites: You'll need Claude Desktop (latest version) and Python 3.10+. Install the uv package manager from PowerShell: C:\Users\Admin>powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex" Setting up in five steps: Step 1 Create the project with uv St...

As organizations accelerate their adoption of AI, one challenge consistently emerges: how do we efficiently process and understand the massive volume of documents stored across PDFs, Word files, PowerPoint presentations, spreadsheets, web pages, and other formats? While much of the conversation focuses on Large Language Models (LLMs), Vector Databases, and AI Agents, document ingestion often remains the weakest link in the architecture. This is where MarkItDown MCP comes into play. MarkItDown is an open-source project from Microsoft that converts diverse document formats into clean, structured Markdown that AI systems can understand and process effectively. When combined with the Model Context Protocol (MCP), it becomes a powerful document-processing service that can be consumed by AI agents, RAG systems, and enterprise applications. What is MarkItDown? MarkItDown is a document conversion tool designed specifically for AI workloads. It converts: PDF documents Microsoft Word documents ...