What is an agentic workflow?
Call it a workflow and you can test it before launch. Call it an agent and you are guessing what it will do next.
Agentic workflow
An agentic workflow is a sequence of steps a developer defined in advance. One or more steps calls an AI model to do the work: extract data, draft text, classify a request. The path through the steps is fixed, and only what happens inside a step is left to the model.
Anthropic's engineering team drew this line in a 2024 post on building effective agents. Workflows are "systems where LLMs and tools are orchestrated through predefined code paths." Agents are "systems where LLMs dynamically direct their own processes and tool usage." A workflow has a wiring diagram you could draw before you write a line of code.
Read a transcript, pull five fields from it, file them under the right customer. That order does not change between runs. An agent, by contrast, is handed a goal and works out its own route. It might use two tools or six, retry, or stop and ask a person. Two identical requests can take different paths through an agent. They cannot through a workflow.
Most business problems want the workflow
A fixed path is cheaper to run and possible to test. You can write a case for every branch, check the output against a known-good answer, and know exactly what happens on a bad input. An agent's freedom to improvise is the whole feature, but it is also the reason you cannot write that same test suite. The path itself is part of what you are testing.
Vendors selling agents have a reason to blur this line. "Agentic" sounds like more product than "workflow", so a deterministic pipeline gets relabelled on the way to a pitch deck. Ask what happens on request two thousand and one. If the answer is "the same five steps, in the same order," you are looking at a workflow wearing a bigger word.
Little Tree Confections, a bakery, runs meeting transcripts through an n8n pipeline we built. A transcript arrives, AI refines it, tasks land in the right department's ClickUp list. That sequence never varies, and it was built that way on purpose. A system free to improvise the order would only add ways to route a task to the wrong department.
- 01Fixed path: every branch can be tested against a known input before launch.
- 02Cost is predictable, because the number of model calls per run does not change.
- 03A wrong step is easy to locate, because the step order is not itself a variable.
- TriggerA transcript, form, or scheduled run starts it.
- ExtractAI pulls structured fields from unstructured input.
- RouteFixed logic sends the result to its destination.
- WriteThe task or record lands in the target system.
- LogEvery run is recorded the same way.
Every box runs on every request, in this order. Swap any box for an agent and the diagram stops being accurate.
Common questions
01Is an agentic workflow the same as an AI agent?
No. A workflow follows a path a developer wrote in code, even when a step inside it calls an AI model. An agent decides its own path toward a goal. The practical difference is testability: a fixed test suite covers a workflow's branches, and no such suite covers a path the system chooses itself.
02When does a workflow become an agent?
When the system starts choosing which tool to call next, rather than running tools in an order you fixed. Adding an if-statement does not make a workflow agentic. Handing the model a goal and letting it decide the sequence does.
03Can a workflow use more than one AI model?
Yes. A workflow can use several models at different steps: one for extraction, another for drafting, another for classification. What keeps it a workflow is the order those calls happen in. The code fixes that order; no model chooses it at run time.
04Why would anyone choose a workflow over a more capable agent?
Because most tasks have a known shape, and a known shape is cheaper to run and easier to trust. An agent earns its cost when the task genuinely varies enough that no fixed sequence covers it. That is fewer tasks than most pitches suggest.
