You open a repository or a vendor’s documentation page. A Python script with a simple while loop is pitched as an “agentic workflow.” A standard Retrieval-Augmented Generation pipeline that searches a vector database is marketed as an “agentic system.” A customer service chatbot executing a hardcoded decision tree is sold as an “autonomous agent.”
The industry has taken a specific architectural concept and stretched it until it covers anything containing an LLM call. When a term means everything, it describes nothing. To evaluate what you are actually deploying, buying, or building, the distinction must be precise.
There is a structural difference between a model generating text and a system operating autonomously. Distinguishing between these patterns requires looking at who — or what — owns the control flow.
The precise distinctions
Plain LLM calls. The baseline. A stateless function execution. Input text goes in, output text comes out. There is no loop, no memory between executions, and no ability to take action. It is a single-shot transformation, like passing a payload to a summarisation endpoint. The model does exactly what is requested and terminates.
Workflows. This is where the confusion usually begins. In a workflow, a human engineer predefines the execution path. The order of operations, the branching logic, and the available tools are hardcoded in advance.
The LLM is used as a sophisticated functional component within that path — perhaps to categorise an incoming ticket, extract entities from a document, or generate text to slot into predefined steps. The model executes tasks, but it does not decide what happens next. The control flow remains entirely human-authored. If an API call fails, the script handles the error according to human-written fallback logic. The LLM acts as a reasoning engine, but strictly within the boundaries of a switch statement.
Agents. An agent operates on a fundamentally different control flow. Instead of following a predetermined path, an agent executes a loop: perceive, reason, act.
It is given a goal, a set of tools, and an environment. It observes the current state, decides which tool to use, evaluates the result of that action, and decides its next step. The crucial nuance is ownership of a scope. An agent is not executing a single one-shot task; it is managing a domain end-to-end.
If an agent attempts to query a database and receives a schema error, it reads the error, infers the correct schema, adjusts its query, and tries again. It decides what to do next based on what it just observed. The control flow is dynamically determined by the model in real time.
Agentic systems. This is the structural layer above the individual agent. An agentic system involves two or more agents coordinating to resolve a larger objective. Each agent owns a distinct scope. One agent might specialise in navigating internal documentation and retrieving technical specifications, while a second agent evaluates those specifications against a customer’s requested architecture. They pass state, context, and results between them.
Framing the terminology
The boundary between these definitions is actively being mapped by practitioners. Anthropic’s “Building Effective Agents” draws this line clearly, distinguishing between strictly orchestrated workflows and autonomous agents based entirely on who controls the routing and next-step logic. For engineering teams looking at the broader structural patterns, Biswas and Talukdar’s Building Agentic AI Systems offers a practical look at how multi-agent coordination is designed and managed in production.
When a software vendor says “agentic,” the immediate engineering question should be: which of these four is it?
Most features marketed as “agentic” are actually LLM-augmented workflows. That is not a technical failure. Workflows are predictable, highly useful, and often exactly what a production use case requires when the cost of a hallucinated action is high.
The failure is lexical. The word is doing marketing work, inflating a standard software sequence into something it is not. Knowing the difference lets you look past the product marketing and evaluate the actual control flow running underneath.
Once the term is pinned down, the next question is how an agent actually thinks inside that loop. That is where the architecture guide picks up.