Ravi Kumar Blog Posts

Enable AI-powered access to your Pega Investigative Case Management data directly from Claude prompts using the Model Context Protocol (MCP) and OAuth 2.0. What is MCP and why connect Pega to Claude? The Model Context Protocol (MCP) is an open standard that lets AI assistants like Claude securely call tools and read data from external systems — all from a natural-language prompt. Think of it as a universal adapter between your enterprise systems and AI. Pega Infinity V25.1.3 ships with a first-class MCP service built on its existing REST framework. Once enabled, Claude can: Query and summarize investigative cases without switching apps Retrieve assignment details and case status on demand Trigger Pega workflows from conversational prompts Version requirement: This guide is based on Pega Infinity V25.1.3 (Community Edition) with the Rule-Service-MCP rule type. Earlier versions may not include native MCP support. Architecture overview The connection uses three layers: Claude as the AI c...

A practical guide to connecting Pega Blueprint workflows to Claude using MCP (Model Context Protocol) Introduction Pega has introduced MCP Agent Integration capabilities in Pega Blueprint, enabling workflows to be exposed as MCP tools that AI clients such as Claude can invoke directly. This creates a powerful bridge between business processes designed in Pega and conversational AI experiences. In this post, we'll walk through connecting a Pega Blueprint application to Claude Desktop using the built-in MCP Agent integration. What You'll Build? Expose Pega Blueprint workflows as MCP tools Generate a secure MCP endpoint URL Configure Claude Desktop with the custom connector Invoke Pega workflows directly from Claude Step 1: Open the MCP Agent Integration Screen In Pega Blueprint, open your application and navigate to the MCP Agent preview. Here I am using the existing Provider Credentialing BP and took the snapshot of it in Preview mode. Notice the following elements: MCP Agent...

This guide walks you through building a real, working weather assistant using N8N (a workflow automation tool) and an MCP server (a way to give AI agents special tools/skills). By the end, you'll have a working chatbot you can talk to via a simple API call, and it will tell you the weather for any US city. No prior experience required. Let's go. What Are We Building? A weather AI agent that: Accepts a question like "What is the weather like in Austin, TX?" via an API call (webhook) Uses an AI model (Google Gemini) to understand the question Calls a weather tool to fetch real weather data Returns a human-readable weather summary Here's what the final workflow looks like in N8N: What You Need Before Starting N8N Community Edition installed locally ( https://n8n.io ) Node.js installed on your machine ( https://nodejs.org ) uv package manager installed ( https://github.com/astral-sh/uv ) A Google Gemini API key (free at https://aistudio.go...

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...