Do Agents Still Need RAG with Million-Token Context Windows?
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.
What Is AI Token Economics - Tokenomics?
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.
What is Multi-Hop Retrieval?
Multi-hop retrieval runs several search passes in sequence to answer questions whose supporting evidence sits in separate documents. This post reviews what it is and how Progress Agentic RAG integrates multi-hop retrieval into its architecture.
How Does RAG Help AI Agents?
AI agents can plan tasks, call tools and adjust their approach as the work unfolds, but the knowledge they start with is frozen at training time. In this post, we'll discuss how RAG helps AI agents retrieve current, relevant and verifiable information before they reason or act, so their decisions rest on real sources instead of model memory.
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