Agentic workflow
An agentic workflow is a defined sequence of steps in which one or more AI models make decisions or produce content at specific points, while the order of the steps is fixed by design. It sits between rules-based automation and a fully autonomous agent, keeping the process predictable.
Also known as: AI workflow, Model-in-the-workflow, Guided agent
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Why agentic workflows matter
They are the pragmatic middle ground, and in our experience the design most businesses actually need. A fully autonomous agent that chooses its own path is powerful and harder to predict, test and govern. A rules-only workflow is predictable but cannot handle steps requiring interpretation.
An agentic workflow keeps the sequence under your control while letting a model handle the judgement inside individual steps. That means you can test it the way you test any process, and a failure is traceable to a specific step rather than to a decision nobody can reconstruct.
How an agentic workflow works
You define the stages, as you would any workflow automation: what triggers it, what happens in what order, who approves what, what happens on an exception. Then at the stages requiring interpretation, a model does the work — reading the document, classifying the request, drafting the response.
The model has no say in what comes next. That is the defining constraint and the source of the benefits: each step has a defined input, a defined output you can validate against a schema, and a defined failure path. Where a step is sensitive, a human in the loop approves before the workflow continues.
Agentic workflow vs an AI agent
The difference is who decides the order. In an agentic workflow, you do, at design time. With an AI agent, the system does, at run time, based on what it finds.
That makes agents the right answer when the necessary steps genuinely vary case by case, and workflows the right answer when they do not. Workflows are cheaper to build, easier to test and simpler to explain to an auditor. A good deal of what is marketed as agentic AI is, on inspection, an agentic workflow — which is not a criticism, because it is usually the more sensible design for a defined business process.
When you need one
Choose this shape when the process has a known sequence but some steps need reading, judgement or writing. Invoice processing, support triage, onboarding checks and document review are typical: the stages never change, but the content varies every time.
Move to a full agent only when you can point to cases where the correct next step genuinely depends on what was discovered. Start with the workflow if you are unsure — it is straightforward to grant a step more latitude later, whereas retrofitting predictability onto an autonomous design usually means rebuilding it.
Agentic workflow: common questions
Who decides the order of steps. In an agentic workflow you fix the sequence at design time and a model handles judgement within individual steps. An AI agent decides its own sequence at run time based on what it finds. Workflows are cheaper to build, easier to test and simpler to audit.
Less flexible, not less useful. For a process whose stages never change, fixing the sequence is an advantage rather than a limitation: it is testable, explainable and predictable in cost. Much of what is marketed as agentic AI is an agentic workflow, and usually should be.
Yes, and it is one of the main reasons to choose this shape. Because the sequence is fixed, you can place an approval at exactly the point where an action becomes irreversible, and the workflow simply waits. That is harder to guarantee when a system chooses its own path.
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