AI leaders are under pressure to turn unstructured content into measurable outcomes quickly. But most DIY Retrieval-Augmented Generation (RAG) projects stall in the messy middle: connectors, security, data governance, evaluation and ongoing MLOps. On top of that, the technical stack is getting more complex—moving from basic RAG to agentic RAG and now Model Context Protocols (MCPs) that broker connections to enterprise systems.
In this session, we’ll compare internal “build” paths with Progress Agentic RAG-as-a-Service, showing how a managed, modular stack accelerates time to value while derisking production. We’ll demystify the core components, covering what RAG is beyond the buzzwords, how agentic RAG technology extends it with agents and workflows and where MCPs fit into the architecture. From there, we’ll have a pragmatic “build vs. buy” discussion, then look at how the Progress Agentic RAG solution goes beyond from ingestion to verifiable, explainable answers with evaluation and governance baked in, so you can scale confidently. Topics include:
- Why DIY RAG stalls AI projects time and time again
- How the building blocks of a RAG implementation fit together
- What the pros and cons are of building vs. buying
- What Progress RAG-as-a-Service actually delivers