What is an AI workflow?

The question

What is an AI workflow?

The short answer

An AI workflow is a repeating job broken into fixed steps, where an AI model handles one or two of them, such as drafting, sorting or summarizing, and plain rules or a person handle the rest. You decide the steps in advance. That is what separates it from an AI agent, where the model decides its own steps as it goes.

Source: Building effective agents, Anthropic, Optimizing your website for generative AI features on Google Search, Google Search Central, Introducing Search Generative AI performance reports in Search Console, Google Search Central blog

A team decides to "use AI". Someone opens a chat window, pastes in a messy spreadsheet, and gets a nice paragraph back. Next week nobody can repeat it, because nobody wrote down what they pasted or what they asked.

That is a demo. A workflow is the version that runs again on Monday.

What is an AI workflow?

An AI workflow is a job you already do on repeat, written down as steps, with an AI model doing the step that needs judgment.

Anthropic's engineering post on building effective agents gives the cleanest definition we know. Workflows are "systems where LLMs and tools are orchestrated through predefined code paths." In plain terms: you decide the order of steps, and the model fills in one of them.

Every workflow we build has four parts:

1. The material: what goes in (an email, a form, a report).
2. The tool: the model and anything it is allowed to read.
3. The check: what a person or a rule verifies before anything leaves.
4. The artifact: what comes out (a draft, a tagged row, a summary).

If you can't name all four, you don't have a workflow yet.

What is the difference between an AI workflow and an AI agent?

The definitions come from the same Anthropic post, which describes agents as systems where models "dynamically direct their own processes and tool usage." It also warns that autonomy brings "higher costs, and the potential for compounding errors." The rest of the table is our framing.

What is an agentic AI workflow?

"Agentic workflow" usually means a workflow with one step where the model gets more freedom: it may choose which tool to call or loop until a condition is met, inside fixed walls.

That middle ground is useful. Google's guide to generative AI features in Search describes AI agents as "autonomous systems that can perform tasks on behalf of people," such as booking a reservation. Our read: most business work doesn't need that much freedom. Give the model room on the step that needs it, and keep the rest fixed.

What are some AI workflow examples?

These are jobs we see in almost every team. In each one, the model does one step and a person does the check.

• Email replies. A question arrives. The model drafts a reply using only your approved answers page. A person checks the price and the promise, then sends.
• Meeting notes. A recording goes in. The model writes a summary with action items and owners. The meeting lead fixes names and dates.
• Lead sorting. A contact form arrives. The model tags it by type and urgency. A rule routes it. A person reviews the weekly tags for drift.
• Report drafting. Last month's numbers go in. The model drafts the commentary. The owner checks each number against the source.
• AI visibility check. Once a month, Search Console's generative AI performance report gives impressions in AI Overviews and AI Mode by page. The model writes a short summary of what changed. A person checks it against the report before it goes out.

Notice what none of them do: send, pay or publish on their own.

How do you build an AI workflow?

1. Pick one job that repeats every week. Not the most exciting one. The most boring one with the most hours.
2. Write the steps as they happen today. Who does what, in what order, with which file.
3. Mark the one step that needs judgment. That is where the model goes. Everything else stays as rules.
4. Give the model only what it needs to read. The current price list, not the whole drive. Anthropic's post calls this the "augmented LLM": a model with retrieval, tools and memory, set up for one use.
5. Write the check down. "Price matches the page. No new promises. Names are right." A check nobody wrote down is a rubber stamp.
6. Run it for one week on real work. Count minutes per item before and after, and count errors caught ("2 of 20 drafts had a wrong date").
7. Only then add freedom. If a step really can't be planned, let the model choose there, and test it somewhere safe first.

What is AI workflow automation?

It is the same idea with fewer hands. Steps that were copy-and-paste become automatic: the email lands, the draft appears, the person approves. The check stays. Automation that removes the check is not a workflow, it is a gamble with good branding.

FAQ

Is an AI workflow the same as automation?

Partly. Classic automation runs fixed rules. An AI workflow adds one step that needs judgment, like drafting or sorting, and keeps a check on it.

Does a small team need an AI agent?

Usually not at first. If you can list the steps, a workflow will do the job for less. Agents fit work where the steps can't be known in advance.

What is the simplest AI workflow to start with?

Drafting replies to the same questions you answer every week, from one approved page, with a person approving each one.

What tools does it take to build an AI workflow?

A model you trust, the files it needs to read, and a place for a person to approve the output. The tool matters less than writing down the steps and the check.

How can you tell if an AI workflow is working?

Count minutes saved per item and errors caught, on real work, for at least a week. Not how impressive the demo felt.