RAG and Agentic Workflows

RAG & Agentic Workflows are two prominent shifts that describe how we were deploying our Large Language Models until today. RAG acts as the link between a model’s static training data and the dynamic, proprietary environment of live information. By anchoring the LLM on outside databases, RAG tackles both hallucination and outdated knowledge. When a user asks a question, the system extracts relevant document snippets from the vector database and uses them as context for the model. This guarantees that the produced response is factually motivated from a concrete set of sources instead of being inferred by the model’s internal “best guess”.

Whereas RAG is informed by the criterion of maximising accuracy, Agentic Workflows are motivated to perform best while driven by an ability for autonomy and complex problem-solving. Contrary to a typical chatbot, which follows fixed flows of dialogue, an agentic system views the LLM as a reasoning engine with tool support. They rely on an iterative loop, where the model can plan a sequence of actions, execute them through external APIs, observe the results, and update its strategy in case it seems to fail at their first attempt. It transitions the AI from a passive responder to an active agent that can surf the web, execute code or access multiple databases to solve a high-level goal.

The systems built from these two paradigms are very complex when combined. A robotic RAG system does not just scrape the first document it comes across; it can judge its trustworthiness, decide to look elsewhere if the information is inadequate and use multi-hop reasoning methods to write a final answer. This progress paves the way from simple AI “chatting” to robust AI “doing.”