--- sourceDocument: Brazil Build workflows sourceDocumentLink: https://www.servicenow.com/docs/r/build-workflows Release : - brazil ft:locale : - en-US ft:publication_title : - Brazil Build workflows ft:clusterId : - crworkflow bundleId : - crworkflow workflow : - Creator --- # Use an AI agent action # Use an AI agent action {#ariaid-title1} Release version: Brazil Updated September 10, 2026 ![](https://www.servicenow.com/docs/portal-asset/ico-clock) 3 minutes to read Summarize ![AI sparkle icon](https://servicenow.com/docs/portal-asset/ai-sparkle-icon) Summarized using AI This content was generated using new OpenAI-powered functionality. Results are provided on an as is basis and are not guaranteed to be accurate or complete. ## Summary of Use an AI agent action The "Use an AI agent" action allows ServiceNow customers to incorporate AI agents into their Flow Designer processes. By leveraging flow data, this action runs an AI agent and captures its output for further use within the flow. It requires the Now Assist AI agents plugin and is available as a Flow Designer Gen AI spoke action. Users with appropriate roles such asflowdesigneroradmincan add and configure this action in their flows. Show full answer Show less ## Key Features * **AI Agent Selection:** Choose a preconfigured AI agent installed via the Now Assist AI agents plugin to execute specific tasks. * **Support User Input:** In supervised mode, a designated support user reviews and authorizes the AI agent's work, ensuring compliance with AI security controls. * **Objective and Context:** Provide detailed instructions and contextual information to guide the AI agent's output. * **Execution Modes:** Run the AI agent autonomously without user input or in supervised mode requiring user approval. * **Wait for Completion:** Optionally pause the flow until the AI agent finishes processing and returns outputs. * **Expected Outputs:** Define the data objects the AI agent is expected to return, enabling subsequent flow steps to utilize this information. * **Conversation Label:** Tag conversations in the Now Assist panel for easy identification and management. * **Context Memory:** Pass additional contextual data to influence AI behavior and output. ## Outputs and Integration The action produces several key outputs that can be used in the flow: * **Execution Plan Record:** Contains comprehensive details about the AI agent's objective, tasks, messages, and tool executions. * **Agent Output:** Dynamic object with the expected output values generated by the AI agent. * **Agent Message:** The message text returned from the AI agent. * **Agent Status:** Indicates if the AI agent ran successfully or failed. ## Practical Example Using the Incident resolution details AI agent (which requires the ServiceNow Otto for ITSM plugin and the Resolution notes generation skill), a flow can run in supervised mode where the support user reviews AI-generated incident resolution data via the Now Assist panel. The flow pauses until input is received, then continues using the AI's output to automate follow-up actions such as sending email notifications. ## Why It Matters This action empowers ServiceNow customers to harness AI capabilities directly within workflows for enhanced automation, improved decision-making, and efficient handling of complex tasks. The flexible modes and rich input/output design enable tailored integration of AI agents while maintaining necessary security and oversight. Use flow data to run an AI agent and configure the expected agent output for use later in the flow. ## Roles and availability {#use-an-ai-agent-action__section-subscription-roles} Available as a Flow Designer Gen AI spoke action, which requires the Now Assist AI agents plugin. Users with the flow_designer or admin role can add an action to a flow and define configuration details. ## Inputs {#use-an-ai-agent-action__section-inputs} Provide a value for each input that your action needs. To add dynamic values, you can also select data pills using the pill picker.{#use-an-ai-agent-action__inputs-description} AI Agent : Data type: Reference The AI Agent that you want to use in this flow. You must install the Now Assist AI agents plugin and create and configure an AI agent. For information about installing the Now Assist AI agents plugin, see [Install ServiceNow Otto AI Agents](https://www.servicenow.com/docs/access?context=install-ai-agents-plugins&version=brazil&pubname=brazil-intelligent-experiences&ft:locale=en-US). For information about creating an AI agent, see [Create an AI agent](https://www.servicenow.com/docs/access?context=configure-next-best-action-agent&version=brazil&pubname=brazil-intelligent-experiences&ft:locale=en-US). Support User : Data type: Reference The user who is contacted by Now Assist to review and authorize the AI agent's work when the AI agent runs in supervised mode. Select a user who meets the security criteria defined in the AI security controls. If the AI agents is configured to accept a dynamic user, select a user that has the roles necessary to run the AI agent. For more information about the security controls of an AI agent, see [Define security controls for an AI agent](https://www.servicenow.com/docs/access?context=define-sec-controls-aia&version=brazil&pubname=brazil-intelligent-experiences&ft:locale=en-US). For information about turning on the Now Assist panel, see [Activate the ServiceNow Otto panel standard chat](https://www.servicenow.com/docs/access?context=activate-now-assist-panel&version=brazil&pubname=brazil-intelligent-experiences&ft:locale=en-US). Objective : Data type: String The text directions sent to the AI agent. Wait for completion : Data type: True/False Option to pause the flow until the AI agent finishes running and provides output values. Execution mode : Data type: Choice Option to run an AI agent with or without user input or approval. In Autonomous mode, the action runs the AI agent without any user input or approval. In Supervised mode, the action requires user input or approval to run the AI agent. Expected outputs : Data type: Dynamic Object List of output objects that the AI agent is expected to return. Each expected output is an object that has a label, name, and a data type value. You can use the expected outputs to gather data from the AI agent and then use that data elsewhere in the flow. Conversation label : Data type: String The name that identifies the conversation about running the AI agent in the Now Assist panel. For information about activating the Now Assist panel, see [Activate the ServiceNow Otto panel standard chat](https://www.servicenow.com/docs/access?context=activate-now-assist-panel&version=brazil&pubname=brazil-intelligent-experiences&ft:locale=en-US). Context Memory : Data type: String The context information that you want to send to the AI agent. This context helps determine what output values the AI agent produces. ## Outputs {#use-an-ai-agent-action__section-outputs} You can use these outputs as inputs for other items.{#use-an-ai-agent-action__outputs-description} Execution Plan Record : Data type: Reference The record containing the Objective, Execution Tasks, Messages, and Tools Executions run by the AI agent. Agent Output : Data type: Dynamic Object The object containing the list of expected output values generated by the AI agent. Agent Message : Data type: String The message returned by the AI agent. Agent Status : Data type: String The run status of the AI agent. The AI agent returns success when it runs or failure when it doesn't run. ## Use Incident resolution details AI agent {#use-an-ai-agent-action__example_ark_w45_khc} This example requires installing the ServiceNow Otto for IT Service Management (ITSM) plugin and turning on the Resolution notes generation skill. In this example, the Use an AI agent action is configured to use the Incident resolution details AI agent in a supervised mode. The objective lists data that will help the AI understand the resolution of an incident. Since this example runs in supervised mode, the flow pauses until the support user interacts with the conversation generated in the Now Assist panel. After the support user provides the necessary response, the flow continues running the action and provides it output values. The flow uses these output values to send an email message.