Guide - Single Knowledge Layer, Multiple AI Experiences

Every new AI project usually means rebuilding the same setup: another ingestion pipeline, another vector index and another round of access controls. Over time, teams end up maintaining old work instead of building new capabilities. Progress Agentic RAG works differently. It's a modular, LLM-agnostic knowledge layer that you set up once and keep building on. In this demo, City Municipal Corp. (a fictitious city) runs five AI experiences from a single deployment: - AI-powered website search: residents get cited answers in plain language, not just a list of links - Support chatbot with human handoff: when the AI reaches its limit, the full conversation passes to a live agent in one click - Blog search: fast semantic search with no generation step, limited to editorial content - Internal enterprise search: employees get cited, access-controlled answers from HR policies and handbooks - MCP integration with Claude: Claude can run agentic research tasks and produce cited research briefs, with no custom build All of this runs on two Knowledge Boxes and four named RAG configurations, connected to custom frontends through the API. Try it yourself https://www.progress.com/agentic-rag/free-trial-sign-up Book a demo: https://www.progress.com/agentic-rag/book-a-demo #AgenticRAG #RAG #EnterpriseAI #MCP #AISearch #GenerativeAI #ProgressSoftware