Tag: Agentic RAG

When is traditional search enough, and when do you need to upgrade to RAG search for your website?

Test how your RAG system handles vague, misspelled, random and generally human questions before adding it to your production site. Here’s how.

Introducing the Smart Agent, a new capability in Progress Agentic RAG that breaks complex questions into sub-questions, decides where to look for each one, and keeps searching until the answer is complete. It works on existing setups with no new pipeline or re-indexing. The blog highlights two additions: live data from business apps like Zendesk, Salesforce and ServiceNow through MCP, and a built-in web search tool backed by Perplexity and Google Gemini. Both are cited alongside internal content in a single answer.

AI models can now take a million tokens in a single prompt, so it’s fair to ask why an agent still needs a layer that hunts down the few relevant passages before it answers. Narrowing first is cheaper and more accurate and it keeps you free to change AI vendors later.

AI token economics connects model usage to the real cost of running an AI application. In this article, we’ll trace token consumption through a RAG pipeline and explore how teams can estimate and manage those costs.