Map inputs before actions
An agent workflow should start by listing the inputs the task needs. Missing context creates weak outputs even when the step list looks complete.
- Capture source material, business context, and constraints.
- Separate required inputs from optional references.
- State what the agent should do when context is missing.
Use checkpoints between steps
Checkpoints make a workflow easier to trust. They tell the agent where to verify assumptions, reconcile evidence, and decide whether the next step is ready.
- Check that the task matches the original goal.
- Review claims before writing final output.
- Confirm the final deliverable matches the requested format.
Design for handoff
A useful workflow ends with a result another person can use. The output should include decisions, unresolved questions, and the next action.
- Summarize what changed and why it matters.
- List blockers separately from completed work.
- Give the next operator a clear starting point.
AI agent workflow FAQ
What is an AI agent workflow?
An AI agent workflow is a structured sequence of steps, checks, and outputs that guides an agent through a multi-step task.
How many steps should an agent workflow include?
Use enough steps to make the work clear, but avoid unnecessary detail. Three to seven steps is often enough for a focused task.
Can one workflow support multiple teams?
Yes, if the workflow keeps shared steps stable and lets each team change context, tone, and acceptance checks.