--- sourceDocument: Yokohama Enable AI sourceDocumentLink: https://www.servicenow.com/docs/r/yokohama/intelligent-experiences Release : - yokohama ft:locale : - en-US ft:publication_title : - Yokohama Enable AI ft:clusterId : - platai bundleId : - platai workflow : - Platform --- # Configuring Now Assist AI agents # Configuring Now Assist AI agents {#ariaid-title1} Release version: Yokohama Updated October 8, 2026 ![](https://www.servicenow.com/docs/portal-asset/ico-clock) 5 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 Configuring Now Assist AI agents TheNow Assist AI agentsfeature enables ServiceNow customers to configure AI-driven agentic workflows that use AI agents and mapped tools to autonomously execute tasks toward specific goals. These agents leverage record context and searchable content, combined with large language models (LLMs), to plan, analyze, and suggest optimal next steps within workflows. Ensuring up-to-date record data and knowledge bases is critical for achieving accurate and effective AI agent performance. Show full answer Show less ## Configuring AI Agents Before configuring AI agents, it is essential to: * Clearly define the types of tasks the agentic workflow should handle. * Understand the general workflow and interaction between agents. * Use agentic tools with precise, well-written descriptions. Key configurable elements for AI agents include: * **Base plan:** Initial instructions for AI Agent Orchestrator to plan the workflow. * **Role:** Defines the AI agent's identity, including agent reasoning and an auto-generated proficiency description detailing capabilities. * **Instructions:** Step-by-step operational directives for the AI agent, written clearly as an algorithm. ## Configuring Tools for Agentic Workflows Tools are configured to provide specific, singular functionality within workflows. Key points include: * **Functionality:** Each tool should address a single purpose to avoid confusion and improve agent reasoning and performance. * **Tool description:** Must clearly explain the tool's utility, its scope, applicable scenarios, and limitations. This helps AI agents select the appropriate tool and avoid misuse. * **Error messages:** Designed to help AI agents learn from execution errors, improving future task handling and maintaining workflow accuracy. ## Dynamic Orchestrator When an agentic workflow has more than 8--10 AI agents, the dynamic orchestrator optimizes performance by selecting the most relevant agents during the planning phase, enhancing accuracy and efficiency. ## Invoking AI Agent Conversations The AI Agent Background Channel enables invoking AI agents or workflows from the Workspace using the AI Agent Background Provider, which integrates with the Virtual Agent's Custom Adapter Framework. Key configuration steps include: * Setting up channel identifiers in the Provider Channel Identities table for additional conversational capabilities. * Triggering conversations programmatically via the `snaia.AiAgentRunttimeUtil().startAiAgentConversation(request)` API. * Monitoring conversations in the Execution Plans table to review execution details, tasks, messages, and tools used. * Testing workflows via the AI Agent Studio Testing page for insight into AI decision-making and tool usage. ## Interactive vs Non-interactive AI Agents * **Interactive AI agents** request user input during execution fallbacks to proceed with the workflow. * **Non-interactive AI agents** do not seek user input during fallback stages but present results or failure messages to users via Now Assist or Virtual Agent interfaces. The execution mode is defined in the `Execution Plans` table and can be set to Interactive or Non-interactive, influencing how the AI agent handles fallbacks and user interactions. 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. ## Configuring AI agents {#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} ## Configuring the tools for the agentic workflows {#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 ensure 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. ## Dynamic Orchestrator for agentic workflows {#configuring-ai-agents__section_f3x_324_xfc} When there are more than the ideal 8 to 10 AI agents available for an agentic workflow, then the dynamic orchestrator helps identify the agents that need to be considered for executing that agentic workflow in the planning phase. The dynamic orchestrator helps map the right agents to the user's agentic workflow to provide better performance and accuracy. ## 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=yokohama&pubname=yokohama-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=yokohama&pubname=yokohama-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 do not 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 do not 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.