Configure Now Assist AI agents

  • Release version: Australia
  • Updated June 16, 2026
  • 6 minutes to read
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    Summary of Configure Now Assist AI agents

    This guide explains how to configure Now Assist AI agents in ServiceNow to execute agentic workflows using AI-driven agents and mapped tools. AI agents act autonomously toward defined goals by leveraging record context, searchable content, and large language models (LLMs) to plan and recommend the next best actions. Accurate and current record data and knowledge bases are essential for optimal AI agent performance.

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    Preparing for AI agents configuration

    Before configuring AI agents, it is important to:

    • Clearly define the types of tasks the agentic workflows should handle.
    • Understand the general workflow and flow of interactions for the agents.
    • Use well-described, purpose-specific agentic tools to maximize workflow efficiency and reduce redundancy.

    Configurable elements

    The framework includes these key configurable elements:

    • Base plan: Initial instructions for AI Agent Orchestrator at the workflow level.
    • Role: Defines the AI agent’s identity, including reasoning prompts and an auto-generated proficiency description that outlines capabilities based on assigned instructions and tools.
    • Instructions: Step-by-step algorithmic directives guiding the AI agent’s operational flow.

    Tools for agentic workflows

    Tools are central to AI agent functionality and must be:

    • Configured for a single, clear purpose: Multipurpose tools complicate decision-making and reduce performance.
    • Described comprehensively: Tool descriptions should specify use cases, boundaries, applicable workflows, and clarify terminology to avoid misuse.
    • Designed to support error handling: Error messages from tool executions help AI agents learn and correct course, improving accuracy over time.

    Invoking AI agent conversations

    The AI Agent Background Channel enables initiation of AI agent conversations from the Workspace using the AI Agent Background Provider, based on the Custom Adapter Framework from Virtual Agent. Key points include:

    • Creating channel identifiers for custom chat capabilities.
    • Starting conversations programmatically via the snaia.AiAgentRunttimeUtil().startAiAgentConversation(request) API.
    • Monitoring conversations and executions through Execution Plans and AI Agent Studio Testing tools, with detailed logs of tasks, messages, and tool usage.

    Interactive vs. Non-interactive AI agents

    Two execution modes are supported:

    • Interactive AI agents: Engage users for additional input when needed during execution fallbacks.
    • Non-interactive AI agents: Do not prompt users for input but dynamically adjust prompts internally; results and failure messages are displayed in Now Assist or Virtual Agent panels.

    The Execution Mode is controlled via the Execution Plans table. Both modes can run concurrently in the AI Agent Background Channel, supporting various chat panels.

    Multilingual support

    AI agents support multiple languages to improve translation quality by:

    • Tuning system prompts for native language accuracy.
    • Implementing fallback translation strategies.
    • Enabling extensive testing through automated and manual evaluations.

    AI Agent Studio Skills migration

    You can migrate AI Agent Studio skills from the on-glide to the off-glide execution path (Mosaic) by enabling the Off-Glide Enabled setting in the OneExtend Capabilities table for the Now Assist AI Agents application. This migration optimizes skill execution paths.

    Configure the Now Assist 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

    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.
    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.
    • Instructions: Clear directives for the AI agent. Write instructions as a step-by-step algorithm that describes the operational flow for the AI agent.

    Understanding the tools for agentic workflows configuration

    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.
    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.
    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

    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.

    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.

    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

    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 Now Assist 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 Now Assist panel or Virtual Agent.

    Multilingual support

    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.

    AI Agent Studio 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 Now Assist AI Agents application.
    • Set the Off-Glide Enabled to true
    • Select Save.