Agentic

Product truth: If the catalog is inconsistent, an agent’s selections will look arbitrary (“the fallacious shirt,” “the fallacious size,” “the fallacious material”), and trust collapses quickly. Payee truth: Agentic...

like OpenAI’s GPT-5.4 and Anthropic’s Opus 4.6 have demonstrated outstanding capabilities in executing long-running agentic tasks. Consequently, we see an increased use of LLM agents across individual and enterprise settings to perform complex tasks,...

fails in predictable ways. Retrieval returns bad chunks; the model hallucinates. You fix your chunking and move on. The debugging surface is small since the architecture is straightforward: retrieve once, generate once, done. Agentic...

The accountability challenge: It’s not them, it’s you Until now, governance has been focused on model output risks with humans within the loop before consequential decisions were made—akin to with loan approvals...

, also often known as RAG, is a strong method to seek out relevant documents in a corpus of knowledge, which you then provide to an LLM to offer answers to user questions. Traditionally, RAG...

: Why this comparison matters RAG began with a simple goal: ground model outputs in external evidence reasonably than relying solely on model weights. Most teams implemented this as a pipeline: retrieve once, then generate...

-Augmented Generation (RAG) has moved out of the experimental phase and firmly into enterprise production. We aren't any longer just constructing chatbots to check LLM capabilities; we're constructing complex, agentic systems that interface directly...

is Nikolay Nikitin, PhD. I'm the Research Lead on the AI Institute of ITMO University and an open-source enthusiast. I often see a lot of my colleagues failing to seek out the time...