What is an AI agent workflow? A practical guide

An AI agent workflow is a graph of steps in which some steps are AI agents. An agent is a model call that reads its input, may call tools to look things up or act, and returns a result the next step can use. Everything around the agents, such as what starts the run, where data goes and what happens on each branch, stays ordinary, predictable logic.

It sits between two things you probably already know:

• Plain automation moves data between apps by fixed rules. It is reliable, but it cannot read a support ticket and decide how urgent it is.
• A single autonomous agent gets a goal and a bag of tools and decides everything itself. It is flexible, but hard to predict, test and explain.

An agent workflow keeps the skeleton fixed and uses a model only where judgment is needed. That is why it is usually the right shape for work a business depends on.

• A trigger starts the run: a person pressing Run, a schedule, or a call from your own code through the workflow API.
• Agents do the reading and deciding: classify, extract, summarise, draft, choose. In Worfilo that is the Agent node, which calls Claude.
• Tools let an agent reach the outside world: an HTTP API, a connected app such as GitHub or Slack, or an MCP server. See how to give an agent tools.
• Logic routes the result: If and Switch branch on what the agent decided, and Merge joins branches back together.
• An output marks what the run produced, so a caller or a person can use it.

A ticket arrives. One agent decides how urgent it is, and the workflow does the rest by rule:

Trigger (new ticket)
  → Mask PII
  → Agent: classify urgency and topic   → structured output { urgency, topic }
  → Switch on urgency
      high   → Slack: page the on-call channel
      normal → Agent: draft a reply      → Output
      low    → Output

The model makes one judgment call, in a known place, and returns it as structured data. The branch that follows is plain logic, so an urgent ticket always reaches Slack. The full build is in the support ticket triage use case.

An agent workflow fits when:

• You know the steps, but one or two need reading or judgment.
• The work repeats: every ticket, every lead, every morning.
• You need to explain afterwards why a case went the way it did.

It is the wrong tool when a single prompt in a chat would do, or when every step is a fixed rule and no model is needed at all. And a task with no known steps, such as open-ended research, may suit one agent with good tools better than a graph.

When an agent workflow gives a wrong answer, the question is always where. Did the agent misread the input, did a tool return something odd, or did a branch go the wrong way? If all you have is the final output, you are guessing.

That is the idea behind Worfilo: every node reports its status live on the canvas, you can open any node to see what it received and returned, and every finished run can be replayed on the exact graph that ran. Two habits help as much as the tooling:

• Have agents return structured output against a JSON Schema whenever the next step branches on it, instead of parsing free text.
• Iterate on one agent at a time with Test this node, rather than rerunning the whole workflow for each prompt change.

The quickest start is to describe the workflow in a sentence and let Claude draft the graph, then adjust it on the canvas. Or follow Getting started and wire a trigger, an agent and an output by hand. The use cases have complete graphs to copy.