Large model vs. agent: what actually changes

People often blur "the model" and "the agent." The difference is practical, not academic — and it decides what you can trust the system to do.

A plain language model answers from what it learned in training. IBM notes it is "bounded by knowledge and reasoning limitations." An agent, by contrast, uses tool calling to fetch up-to-date information, take actions, and create subtasks to reach a complex goal.

Microsoft frames agents as a layer on top of the model that observes, collects information, feeds it to the model, and together they produce an action plan — or act on it directly when permitted. Model and agent are complementary halves: one thinks, the other perceives and acts.

Not everything needs to be an "agent"

That is why a serious agent setup is mostly about its surroundings — the instructions, tools, permissions, and memory you give it. Those are exactly the assets Berth scans and shows you.