LLM versus agent

An LLM is an AI that predicts and generates text from what it learned in training. One request goes in, one reply comes out, and it knows nothing it was not trained on or handed in the prompt. An agent is a goal-driven loop around that model: it reasons about the goal, plans, uses tools and acts on its own until the goal is met or something stops it.

Same model, different shape

	LLM call	Agent
Driven by	A prompt	A goal
Steps	One	As many as it decides
Tools	None	Calls them, reads the results
State	Only what is in the prompt	Memory across steps and sessions
Decides what is next	The caller	The model

The last row is the real difference. The model is no longer answering, it is steering.

Workflow or agent

Anthropic draws a useful line. On one side, the path is fixed in code:

Workflows are systems where LLMs and tools are orchestrated through predefined code paths.

On the other, the model holds the steering:

Agents, on the other hand, are systems where LLMs dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks.

The LangChain docs put the same idea in one sentence:

An agent is a model calling tools in a loop until a given task is complete.

Most useful systems sit between the two. A fixed sequence of steps with a model at each one is a workflow, and often the better choice, because it is predictable and cheaper (see flow engineeringFlow engineeringDesign the task as a flow of steps: a model where judgement is needed, plain code everywhere else.Opens in a new tab). Handing the model the steering is worth it when the number of steps cannot be known in advance. The properties that make a loop an agent are in the next noteAgent design principlesFour properties that make a loop an agent: goal-directed, reactive, stateful, autonomous.Opens in a new tab.