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Use Agent when you want a workflow step to call an agent. The step sends a message to that agent. The agent’s own instructions, model, and tools apply. The agent can be one of your saved workspace agents, or an agent built just for this step. When your AI assistant builds a workflow for you, it usually builds each agent for its step, so your agents list only holds the agents you made yourself. This is different from AI Prompt, which configures a one-off LLM call on the step itself.

Configuration

Pick an agent from the searchable list. Only active workspace agents appear. After you select one, the settings sheet shows a short summary (model and tool count) and a link to edit the agent.

Agents built for this step

An agent built for this step shows Built for this step. It isn’t in your agents list. under its name. It runs like any other agent and can use web search tools. To change it, click Edit agent. Fetch Hive saves it as a normal agent in your agents list, points this step at it, and opens it in the agent editor. From then on it works like any saved agent, so you can also add connected apps, knowledge bases, or other tools. Picking a saved agent from the list replaces the agent built for this step. Write the message the agent should receive. Use Insert Variable to pass earlier step output into the message. Credits for this step are charged on the agent run itself, not as a separate workflow-step fee. The workflow request still shows that run’s tokens, cost and tasks on the agent step and in the request totals, so you see the whole workflow’s usage in one place. Nothing is charged twice.

Output

This step stores the agent’s reply as the step output.
For example, if this is step_2:
Later steps can insert that text with the variable picker.

Example

Add Agent from the workflow step picker. Select a support or research agent you already configured. Set Message to something like:
Click Run in the step header to test. The agent’s reply appears in Output, and later steps can reference it.

See also