A process can combine very predictable steps with tasks that change every time. Understanding that difference helps you decide how to automate it.
The practical question is what each step needs: a rule, interpreting information or a decision that should stay with a person.
A workflow organises defined steps
A workflow can be triggered when an email arrives, a record is created or a status changes. From that event on, it follows a sequence with known rules.
For example: receive a document, save it to a folder, extract its data and prepare a row for review. Part of the flow can use AI, even though the sequence is defined in advance.
An agent works with bounded decisions
An agent can interpret context and choose between available actions to move towards a goal. That capability needs clear limits.
You need to define which tools it can use, what information it can access, when it must stop and which actions need approval.
Both can be part of the same solution
Imagine an incident-handling process. A workflow logs the request and looks up the customer's data. AI interprets the message and drafts a reply using approved information. A rule sends cases that don't meet the defined criteria for review.
The design focuses on what each step is responsible for and how its result is checked.
Design the exceptions too
An automation must cover what happens when data is missing, a tool doesn't respond or the information is ambiguous.
- Define which situations can be retried.
- Set when the team should step in.
- Keep a log of the actions taken.
- Prevent a duplicate run from repeating sensitive actions.
These details are part of the process from the design stage.
The judgement lies in the work
First describe the task, the context and the expected result. Then decide which combination of rules, AI and human review lets you carry it out with the quality required.
That's the starting point of AI-Automation: connecting tools and people around a process that works in real operations.