---
sourceDocument: Brazil Enable AI
sourceDocumentLink: https://www.servicenow.com/docs/r/intelligent-experiences

 Release :

    - brazil

ft:locale :

    - en-US

ft:publication_title :

    - Brazil Enable AI

ft:clusterId :

    - platai

bundleId :

    - platai

workflow :

    - Platform


---

# Configure

# Configure AI agents {#ariaid-title1}

Release version: Brazil  
Updated October 9, 2026  
![](https://www.servicenow.com/docs/portal-asset/ico-clock) 6 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 Configure AI agents

Configuring AI agents in ServiceNow enables the execution of agentic workflows where AI agents act toward specific goals using configured tools.
These agents leverage record context and searchable content combined with large language model (LLM) instructions to plan and analyze tasks, producing business logic-driven actions.
Ensuring your records and knowledge base contain the latest accurate data is critical for optimal AI agent performance.
Show full answer Show less  

## Preparing for AI agents configuration

Before configuring AI agents, clearly define the tasks your agentic workflows will handle and understand their general flow. Use well-described, single-purpose tools to avoid performance issues. Planning reduces redundant agent creation and maximizes efficiency.

## Configurable elements

* **Base plan:** Initial instructions set at the workflow level for AI Agent Orchestrator planning.
* **Role:** Defines the AI agent's identity, including reasoning prompts and an autogenerated proficiency description that outlines capabilities based on role, instructions, and tool descriptions.
* **Instructions:** Clear, algorithmic step-by-step directives guiding the AI agent's operational flow.

## Understanding and defining tools

Tools must have a single clear function with comprehensive natural language descriptions that specify their utility, applicable scenarios, and limitations. Avoid multipurpose or multi-mode tools to prevent confusion and reduce runtime. Clearly describing tools supports the AI agent in selecting the appropriate tool and handling errors effectively. Error messages serve as feedback for the AI agent to improve future executions.

## Invoking AI agent conversations

Use the AI Agent Background Channel and the AI Agent Background Provider (based on the Custom Adapter Framework) to initiate AI agent conversations within ServiceNow Workspace. Conversations are triggered via a specific API and logged in the Execution Plans table, allowing you to review execution tasks, messages, and tool usage. Testing and monitoring can be performed through AI Agent Studio.

## Interactive vs Non-interactive AI agents

* **Interactive agents** seek user input when fallback scenarios occur during execution.
* **Non-interactive agents** do not request user input on fallback; instead, they adjust prompts dynamically and present outputs or failure messages through interfaces like the Otto panel or Virtual Agent.

The execution mode is configurable in the Execution Plans table and supports concurrent runs in background channels.

## Multilingual support

AI agents support multiple languages with tuned system prompts for native translations and dynamic translation strategies where native support is unavailable. This includes thorough automated and manual testing to ensure translation quality.

## AI Agent Studio Skills migration

You can auto-migrate AI Agent Studio skills from the on-glide to the off-glide execution path by enabling the "Off-Glide Enabled" setting in the OneExtend Capabilities table for the ServiceNow Otto AI Agents application, facilitating skill migration to Mosaic.  
Configure AI agents to execute agentic workflows with AI agents and mapped tools.  
AI agents follow your instructions and act toward a specific goal and outcome by using the tools that you configure for those agents. By using the context of your record and your searchable content, AI agents can plan and analyze the task with a business logic that is combined with the instructions that are sent to large language models (LLMs) that suggest the next best action to be taken.  
Note:  
Make sure that your record data and knowledge base have the latest accurate information for the best results.

## Preparing for AI agents configuration {#configuring-ai-agents__section_n34_2l1_k2c}

Prerequisites
:   By making a plan, you can improve your AI agent performance and result quality. When you have a solid foundation of what you want to build, you can minimize creating redundant agents and maximizing the efficiency
    of your existing ones. Before you send instructions to your AI agents, make sure that you follow these prerequisites:

    * Have a good idea of the different kinds of tasks that your agentic workflow should be able to handle.
    * Understand the general flow for your agentic workflow and agents.
    * Use agentic tools with well-written descriptions.
    {#configuring-ai-agents__ul_sbt_gl1_k2c}

Configurable elements
:   Instruct the agentic workflows and AI agents through the following elements within the framework:

    * Base plan: Instructions to the AI Agent Orchestrator for the initial planning procedure that is configured at the agentic workflow level.
    * Role: Clear identity of the AI agent that includes these elements:
      * Agent reasoning: When a role is added to each reasoning prompt, it provides a sense of identity to the content that is generated by the LLM.
      * Agent proficiency: An LLM-generated description of what an agent is capable of, including the content from the role, instructions, and the descriptions from the tools that are assigned to the AI agent.  
        Note:  
        The agent proficiency is auto-generated.
      {#configuring-ai-agents__ul_dxz_4mc_k2c}
    * Instructions: Clear directives for the AI agent. Write instructions as a step-by-step algorithm that describes the operational flow for the AI agent.
    {#configuring-ai-agents__ul_ybv_pq1_k2c}

## Understanding the tools for agentic workflows configuration {#configuring-ai-agents__section_m43_f4c_k2c}

Define the procedure to build functional tools for your agentic workflow with the following three elements:

Functionality
:   What an AI agent contributes to the agentic workflow. Configure the tools with a single purpose. Multipurpose tools can cause a problem for the agents for the following reasons:  
    * Multipurpose tools are harder for the AI agent to reason through and determine when to use the tool. If a tool can be used for more than one purpose, the AI Agent Orchestrator has to determine which purpose is most applicable, which can decrease your AI agent's performance by increasing the runtime.
    * The tool description must be comprehensive enough to account for all the scenarios for the usage of the tool that is being defined.

    {#configuring-ai-agents__ul_acw_xwc_k2c}  
    Note:  
    Don't use tools that can operate in different modes. Instead, configure your tools as the solution to a singular problem for a scenario.

Tool description
:   Natural language descriptions that describe the utility provided by the tool. Make sure that you define the scope and limits of the tools clearly to help verify that the tools are picked for the appropriate
    scenarios in the following ways:  
    * Provide a description of what the tool is supposed to do.
    * Describe the scenarios where the tool can be called. Include the specific agentic workflows and tasks where the tool and its functionality can be used.
    * Explore the scenarios where the tool is explicitly not useful but an AI agent can confuse the tool as being useful.
    * Explain the terms that are being used in the preceding cases. For example, if you have a tool for assigning a role to a user, you must explain what the role is in the agentic system of the given instance.
    {#configuring-ai-agents__ul_qtj_5xc_k2c}

Error messages

:   An AI agent operates through trial and error. For example, an error message about an execution that accidentally ran incorrect tools can help the AI agent reach more valid conclusions in the future. Error messages offer an AI agent a chance to reflect and explore other options.Understanding the scenarios where the tool can go wrong can help the AI agent with keeping the execution on track.

## Invoke Conversations with AI Agent Background Channel {#configuring-ai-agents__section_z1j_hgf_zfc}

The AI Agent Background Channel helps you to invoke AI Agent or agentic workflow execution from the Workspace. Use the AI Agent Background Channel associated with the AI Agent Background Provider to invoke conversations. The
AI Agent Background Provider is based on the Custom Adapter Framework from Virtual Agent. For more information, see [Configure a provider for your custom chat integration](https://www.servicenow.com/docs/access?context=create-provider-va-cccif&version=brazil&pubname=brazil-conversational-interfaces&ft:locale=en-US).

Create a channel identifier in the Provider Channel Identities table \[sys_cs_provider_application\] to add any additional conversational capabilities to your own provider application and get a new inbound ID that allows for
customization. For more information, see [Create a channel identifier for your custom chat integration](https://www.servicenow.com/docs/access?context=create-channel-id-va-cccif&version=brazil&pubname=brazil-conversational-interfaces&ft:locale=en-US).

To start a conversation, trigger the flow using the sn_aia.AiAgentRunttimeUtil().startAiAgentConversation(request) API in the Script Include (sys_script_include) of the AIAgentBackgroundProvider and select
Run Script. When the Script execution status indicates Success, the conversation begins in the order of utterances defined in the Script.

Conversations that are invoked for executing an AI agent are logged in the Execution Plans \[sn_aia_execution_plan\] table. Open the conversation record to confirm the device type as AI Agent Background. Open the execution record to see the Execution Tasks, Messages, and the Tools Executions used to execute the AI agent.

You can also see the entire execution steps on the AI Agent Studio Testing page by copying the execution plan record's \[sys_Id\] and testing it. On the Chat responses tab, in the AI agent decision logs, you can see the AI agent
details and the tools it used to resolve the issue.

## Interactive and Non-interactive AI agents {#configuring-ai-agents__section_yl3_1zr_zfc}

The Interactive AI agents reach out to users for information when there is a fallback in the execution process, and the AI agent re-triggers the flow.

The Non-interactive AI agents don't reach out to the user at any fallback stage in the execution process. When the AI agent needs user information, it takes the dynamic prompt approach using the ReAct layer, where the prompt
of the ReAct will change based on the execution mode of the AI agent or agentic workflow. Therefore, in the Non-interactive execution, the reach fallback options don't have to collect input from a user as a fallback option.
However, the output of the AI agent or agentic workflow will still need to be presented to the user, and in any execution failure scenario, a message in the ServiceNow Otto panel or Virtual Agent is shown.

To implement the Non-interactive execution, the Execution Mode field is added in the Execution Plans \[sn_aia_execution_plan\] table, where the execution mode can be Interactive or
Non Interactive based on the given runtime parameter.

You can run the AI agents and agentic workflows concurrently in the AI Agent Background Channel and in Non-interactive mode where the background execution allows AI agents to operate with any chat
panel like ServiceNow Otto panel or Virtual Agent.

## Multilingual support {#configuring-ai-agents__section_ydj_hjj_hhc}

You can leverage multilingual support for AI agents across languages for better translation quality to:

* Tune system prompts for native translations.
* Implement dynamic translation strategies when native support is unavailable.
* Provide extensive testing via automated and manual evaluations.
{#configuring-ai-agents__ul_yfy_s3j_hhc}

## AI Agent Studio Skills migration {#configuring-ai-agents__skills-migration}

You can auto-migrate all the AI Agent Studio skills from on-glide execution path to the off-glide execution path by setting the Off-Glide Enabled to true, to enable the skill migration to Mosaic. To do this:

* Navigate to the OneExtend Capabilities \[sys_one_extend_capability.list\] table.
* Find the ServiceNow Otto AI Agents application.
* Set the Off-Glide Enabled to true
* Select Save.