An agentic workflow is a multi-step process where an AI agent plans the steps, picks tools, acts and adjusts based on what comes back, instead of following a script written in advance. In this post, we walk through how that loop runs, where it beats traditional automation, and the guardrails it needs in production.
RAG changes what a model knows by retrieving from a live knowledge base, while fine-tuning changes how a model behaves by training it on curated examples. In this post, we compare them in terms of accuracy, cost and citations, and show when to reach for each.