One of the most common misunderstandings in modern AI systems is this:
If an agent follows steps, it must be a workflow.
This is false.
A workflow is deterministic by design.
An agent is probabilistic by nature.
A workflow knows exactly what comes next because it was defined that way.
An agent only knows what sounds like the next step.
When an agent appears to “run a workflow,” what is really happening is one of two things:
The workflow is hard-coded outside the model
Or the agent is guessing and hoping the guess looks reasonable
The first case is stable.
The second case is dangerous.
Confusing these two leads to systems that look correct in demos but collapse under real-world variability.
A workflow enforces order.
An agent imitates order.
Imitation works—until it doesn’t.
And when it fails, it fails quietly, confidently, and without warning.
That is why replacing workflows with agents is not innovation.
It is regression disguised as intelligence.
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