Configuring Now Assist AI agents

  • Release version: Xanadu
  • Updated June 24, 2026
  • 3 minutes to read

    • 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

    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.

    Configuring the tools for the agentic workflows

    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 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.
    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.