Agentic Workflow
Also called Agentic automation
A process where AI models carry out several steps of a task, using tools and checking results, rather than answering a single prompt.
What is an agentic workflow?
An agentic workflow is a multi-step process in which one or more AI models do real work along the way: reading inputs, calling tools, making decisions and checking their own output. A support workflow might read a new ticket, look up the customer's order, draft a reply, check it against the refund policy, and send it to a person for approval.
How does it work?
Agentic workflows sit on a scale from fixed to flexible:
- Fixed steps: prompt chaining, where each step is set in advance and the AI does one job per step.
- Routing: an early step decides which path a task takes, such as refund, bug report or sales lead.
- Loops and checks: one step produces a draft and another reviews it, repeating until it passes.
- Agents: the model decides the steps itself, choosing tools until the goal is met (AI agent).
Tool calling connects each step to real systems, and orchestration coordinates them when several agents are involved.
What is it commonly confused with?
- An AI agent. An agent is one possible part of a workflow. Many reliable workflows use fixed steps with no autonomous agent at all.
- Traditional automation. Classic automation follows rules exactly. Agentic workflows handle messy inputs such as emails, documents and free text, at the cost of being less predictable.
Why does it matter?
Most business value from AI comes from workflows rather than one-off chats. A practical rule is to use the simplest design that works: fixed steps are cheaper, faster and easier to test, so add autonomy only where the task really needs it, and add approval points (human in the loop) where mistakes are costly.
