Agentic RAG

Retrieval is the foundation that determines what AI can reason over and what it can’t. This resource explains why retrieval strategy, not model choice, is the key to reducing hallucinations, preserving context, and delivering trustworthy enterprise AI answers.

The data you need is already stored in your CMS. Progress Agentic RAG helps you connect this intelligence to action. These three workflows show how.

If 2023–2024 were the years of pilots and prototypes, 2025–2026 will be about orchestration, governance and scale. The signal across serious researchers is consistent: adoption is widespread and business impact concentrates where companies redesign workflows, measure outcomes and hard-wire trust and controls into the stack. McKinsey reports that ~80% of companies use generative AI (GenAI), yet most still aren’t seeing material earnings contribution, because scaling practices and operating models lag the hype. This gap is a roadmap that can be leveraged by Frontier Firms and individuals looking for an advantage (or many) in today’s AI-powered world.

Discover how agentic RAG combines Retrieval-Augmented Generation with autonomous AI agents to deliver accurate, context-aware and trustworthy responses. Learn why this advanced architecture outperforms traditional RAG for enterprise AI by facilitating dynamic workflows, compliance and efficiency.