Ravi Kumar Blog Posts

  The Problem With "One Big AI" Picture a health insurance claim landing on someone's desk. Before it gets approved or rejected, a human needs to: Check that all the paperwork is filled in correctly Confirm the treatment is actually covered by the policy Make sure nothing about the claim looks suspicious Understand the medical context well enough to judge if it makes sense Finally, make the call: approve, reject, or send it for closer review That's five different jobs, each needing a different kind of expertise. Now imagine trying to train one person to be equally good at all five. They'd be okay at everything and great at nothing. This is exactly the problem with asking a single AI prompt to "handle insurance claims." You get a jack-of-all-trades that misses details a specialist would catch. The fix? Don't hire one generalist. Hire a team. Meet the Team Here's what I actually built — a little assembly line of AI "employees,...

I recently conducted a simple hands-on exploration of the Connect-MCP rule type in Pega Platform 25.1.3 Community Edition. One of the interesting aspects of this experiment is that no custom code was required—the entire proof of concept was achieved through configuration alone. Depending on future use cases and integration requirements, custom code may become relevant, but this initial research demonstrates the power of a low-code approach. If you're interested in exploring MCP integrations within Pega, you can follow the simple four-step approach outlined in my post and try it out yourself. Very simple steps to build this functionality in your Commu nity Edition for initial learning perspective. This experiment is for education purpose only and this can be extendable later.  For now, I just had this simple usecase for my understanding on this OOTB feature. Step1: Create a simple Connect-MCP and configure with free MCP server. Here is another free server for the folks who want to ...

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...