AI Agent Workflows Explained: How They Work and When to Use One
An AI agent workflow is a process in which an AI agent handles some of the steps, while fixed logic and people handle the rest. Your code sets the overall path, and the agent is given freedom only where the work needs judgment.
For most businesses, this mix works better than either extreme. Pure automation breaks on messy input, while a fully independent agent is harder to predict and control.
AI workflow vs AI agent
The terms get mixed up, so it helps to separate them by asking who controls the path.
| AI workflow | AI agent | AI agent workflow | |
|---|---|---|---|
| Who decides the steps | Your code | The model | Code overall, model in places |
| Predictability | High | Lower | Medium to high |
| Handling of surprises | Limited | Good | Good where it's allowed |
| Testing | Easiest | Hardest | In between |
The building blocks
Most agent workflows use the same five parts:
Trigger. What starts the process: an email, a form, a schedule or an event in another system.
Fixed steps. Ordinary code does anything that follows clear rules, such as validating data or calculating a total.
Agent steps. The model handles what rules can't, like reading a complicated request and deciding what to look up.
Checkpoints. Code checks the agent's output, and a person approves high-impact actions.
Exit. The process ends with a clear result, or an escalation to a person with a summary of what happened.
An example: handling a support request
- Trigger: a customer email arrives.
- Fixed step: code finds the customer's account and order history.
- Agent step: the agent reads the email, checks the policy and proposes a resolution.
- Checkpoint: code checks that any refund is within limits. Larger ones go to a person.
- Fixed step: code issues the approved refund and logs it.
- Agent step: the agent drafts a reply.
- Exit: the reply is sent, and the ticket closes.
Notice that the agent never issues the refund directly. It proposes, and ordinary code carries out the action after checking the limits. The model can't talk its way past that check, even if something in the email has misled it.
How to design your first one
Map the process as it runs today. Talk to the people who do it, and include the exceptions.
Mark each step. Label each one as rules, judgment or high risk. Steps that follow rules become code, steps that need judgment go to the agent and high-risk steps get a person.
Keep agent steps small. A narrow job, like "classify this request and pull out the order number," is much easier to test than "handle support."
Define what passes between steps. Use structured data that code can validate, not free-form text.
Plan for failure. Decide what happens when a tool is down or the agent isn't sure. Escalating to a person is a good default.
Set hard limits. Cap steps, spending and time for each run, so a loop can't continue indefinitely.
Log every step. You'll want to be able to see what happened when a customer asks.
When to use something else
If every step can be written as a rule, you don't need an agent, so use plain automation. If the path truly can't be known in advance, such as in open-ended research, give the agent more freedom and tighter limits on what it can touch.
To understand the full range of options, read our guide to agentic systems.
Talk to us
Have a process that's part rules, part judgment? DPR Software Labs can help you map it and decide what belongs to code, AI and people.