Enable AI Experiences

Starting in Now Assist AI Agents 7.1.x, the API Key credential record determines the header in each A2A request. The flow action no longer adds the header automatically.

Agent2Agent (A2A) is an open standard that enables cross-platform AI agent communication by associating each agent with an Agent Card containing provider information.

Secondary agents are specialized AI agents that handle specific workflow tasks delegated by a primary agent or user. You can connect your ServiceNow agent to other agentic AI model providers using the Agent2Agent protocol.

Advance managed AI assets through controlled stages of governance by completing the reviews, assessments, and approvals that AI asset lifecycle playbooks require.

Investigate and document an incident affecting an AI asset, such as a suspected security breach, data exposure, or policy violation, using a structured case record.

Ask a question or request information related to AI governance, such as a question about a policy, a process, or a specific AI asset.

Respond to a request to acknowledge a compliance policy that applies to a managed AI asset you own or steward.

Make controlled changes to a managed AI asset, or retire an asset that is no longer needed, by submitting a request for AI steward review and approval.

Advance a managed AI asset through its lifecycle by completing the reviews, assessments, or approvals that an asset lifecycle playbook requires.

Open an AI case to investigate and document an incident affecting an AI asset, such as a suspected breach, data exposure, or policy violation.

Submit an AI inquiry to ask a question or request information related to AI governance, such as a question about a policy, a process, or a specific AI asset.

Track a problem identified with an AI asset, such as a performance or compliance gap, and document the action plan for resolving it.

Request an approved, time-bound deviation from a policy or control objective that applies to an AI asset, supported by a justification and a risk assessment.

Track a problem identified with an AI asset, such as a performance or compliance gap, through to resolution with a documented action plan.

Request an approved, time-bound deviation from a policy or control objective that applies to an AI asset.

Address an issue or opportunity by running an unsupervised agent, working through a guided conversation with a supervised agent, or resolving the issue yourself.

Investigate a security concern that AI Control Tower has detected on a managed AI asset, take the appropriate remediation action, and document the resolution.

Evaluate a change request submitted by an asset owner and either approve the change so AI Control Tower can apply it, or reject the change so the asset remains unchanged.

Evaluate an offboarding request submitted by an asset owner and either approve the request to move the asset into the offboarding lifecycle, or reject the request so the asset remains active.

Complete a risk assessment that AI Control Tower generates automatically for a managed AI asset, evaluating its inherent, control, and residual risk.

Address possible security threats, policy violations, and access issues on managed AI assets by acting on the security tasks the system generates for AI stewards and security reviewers.

The agent explains how access control lists (ACLs) are configured. It reads one or more ACLs that govern a table or field for an operation, and describes their roles, conditions, scripts, and admin overrides. The agent is read-only: it doesn't evaluate access, recommend changes, or modify ACLs.

The Access Analysis Agent lets you ask whether a user, group, or role can access something on your ServiceNow instance. It uses guided, conversational workflows in the ServiceNow Otto panel.

The following AI agents are available for Accounts Payable Operations.

Review the actions and their immediate results when performed on duplicate AI assets in the Review duplicates page.

Activate an agentic workflow that come installed with ServiceNow applications in AI Agent Studio.

Deploy a matched AI agent or a new agent to automate a resolution for an identified automation opportunity.

Activate the Information Extraction skill, so AI agents can analyze and extract information from documents using generative AI.

Activate the ServiceNow Otto panel standard chat to enable your agents to use AI skills, such as task summarization or navigation, in a side panel on the user interface.

Activate offensiveness detection to log or block offensive content generated by AI skills and workflows.

View the top actions you can take right away to adopt AI capabilities on your instance.

Add a CIMD client that connects to MCP servers through AI Gateway.

Add AI agents to an agentic workflow so they can work together to accomplish complex tasks.

Add an MCP Server with an API Key in the AI Agent Studio.

Add an MCP client that connects to MCP servers through AI Gateway.

When you add an MCP server in AI Agent Studio, it's automatically discovered and synced to AI Control Tower.

Add an MCP Server by selecting a Connection and Credential Alias record.

Add an MCP Server with OAuth 2.1 in the AI Agent Studio.

Add a Service Catalog to an AI agent in AI Agent Studio so that your users can access conversational catalog items.

Add a catalog item to an AI agent in AI Agent Studio so that your users can access conversational catalog items.

Add a record operation to an AI agent in AI Agent Studio to create, update, look up, or delete records.

Add a record operation tool to an AI agent to enable it to perform record operations on tables in your instance.

Add a deep research tool to an AI agent so it can search multiple sources and provide answers with sources listed.

Add a desktop action as a tool to an AI agent in AI Agent Studio so that AI agents can execute defined path desktop actions for repetitive tasks in desktop and web environment.

Add a desktop action as a tool to an AI agent so that AI agents can automate tasks in desktop and web-based applications.

Upload files for analysis by an AI agent in AI Agent Studio to grant your AI agent access to specialized knowledge.

Upload files for analysis by an AI agent in AI Agent Studio to grant your AI agent access to specialized knowledge.

Add a flow action to an AI agent in AI Agent Studio. Define the flow action to use it as a reusable operation in automating the ServiceNow AI Platform features without having to write code.

Add a flow action to an AI agent in AI Agent Studio so the agent can run it as a reusable operation that automates ServiceNow AI Platform features, without writing code.

Connect a use case to a workflow by adding an integration. Integrations automate document task creation or value extraction based on triggers in the target table.

Add the Interactive View Experience component to a page variant to display agentic AI processes on a record.

Add a Knowledge Graph to an AI agent in AI Agent Studio that uses the structured and unstructured data from different ServiceNow records to enhance the performance of AI agents.

Add a Knowledge Graph to an AI agent in AI Agent Studio that uses the structured and unstructured data from different ServiceNow records to enhance the performance of AI agents.

An MCP Server Console hosts the APIs and tools required by an AI application. It receives and processes calls from MCP Clients to govern ingress traffic and promote secure access to tools. Adding an MCP Server Console in the AI Agent Studio helps you to leverage the Model Context Protocol as a tool in the ServiceNow agentic AI system.

An MCP Server hosts the APIs and tools required by an AI application, enabling it to receive and process calls from MCP Clients to govern ingress traffic and to promote secure and efficient access to tools. Adding an MCP Server in the AI Agent Studio helps you to leverage the Model Context Protocol as a tool in an AI agent.

Add an MCP server with OAuth 2.1 in the AI Agent Studio.

Add an MCP Server with an API Key in the AI Agent Studio.

Add an MCP Server by selecting a Connection and Credential Alias record.

Add an MCP tool to an AI agent in the AI Agent Studio so that your users can access the MCP server.

Add a Model Context Protocol tool to an AI agent in the AI Agent Studio so that your users can access the MCP Server.

Add a search retrieval to an AI agent in AI Agent Studio. Leveraging the Retrieval-Augmented Generation (RAG) enables an AI agent to retrieve and incorporate relevant information from an external source.

Add a search retrieval tool to an AI agent to enable it to retrieve and incorporate relevant information from defined sources.

Create a script to add it to an AI agent in AI Agent Studio. With scripts, you can use the scriptable APIs and back-end integration to support the AI agent.

Add a script tool to an AI agent to enable it to execute custom scripts to perform specialized tasks.

Add a generative AI skill to an AI agent in AI Agent Studio. You can customize the skills to meet the needs of your users in different workflows.

Add a generative AI skill to an AI agent to expand its capabilities with reusable operations and custom functionality.

Add a subflow tool to an AI agent in AI Agent Studio. Subflows are reusable sequences of processing steps that can be called from within a flow.

Add a subflow tool to an AI agent in AI Agent Studio. Subflows are reusable sequences of processing steps that can be called from within a flow.

Add a tool to an AI agent to enable different functionalities and help your AI agents achieve their objectives.

Add a tool to an AI agent to enable different functionalities and help your AI agents achieve their objectives.

In the guided setup for an AI agent, add triggers to run the AI agent automatically when certain conditions are met.

In the guided setup for an AI agent, add triggers to run the AI agent automatically when certain conditions are met.

In the guided setup for an agentic workflow, add triggers to run the agentic workflow automatically when certain conditions are met.

In the guided setup for an agentic workflow, add triggers to run the agentic workflow automatically when certain conditions are met.

Add a Virtual Agent topic to an AI agent in AI Agent Studio so that you can use conversations to get additional information from the user. For example, a conversational topic could be used to let a user select a date range for surveys.

Add a Virtual Agent topic to an AI agent in AI Agent Studio so that you can use conversations to get additional information from the user. For example, a conversational topic could be used to let a user select a date range for surveys.

Add a web search to an AI agent in AI Agent Studio using a third-party search API such as Microsoft Bing or Google.

Add a web search tool to an AI agent to enable it to search the web and retrieve relevant information.

This ServiceNow Otto for Creator agent generates release notes and update set descriptions in natural, human-friendly language for business stakeholders, within a maximum 4000 character limit.

Enhance AI agent learning through episodic memory, enabling agents to improve by learning from past successful interactions.

Monitor usage from inbound agentic connections to MCP servers from the Agentic Usage Overview Dashboard.

If you’re looking for AI best practices, troubleshooting, or other implementation guidelines, select a feature or resource type to discover ServiceNow resources on other relevant websites.

Create a custom metric by specifying event-based condition logic and testing it against live data before saving.

Create custom metrics to define the criteria for measuring AI assistant performance, including custom deflection rates based on event-based condition logic.

Update a custom metric's condition logic, map AI assistants to it, and activate or deactivate it.

Add a value configuration to define the business value metrics calculated for an AI asset, including average time saved per execution and average hourly rate.

Use the Business Value dashboard on the Analytics page to monitor the business value generated by AI assets across your organization, including total executions, time saved, and cost saved.

Use AI admin features on the Settings page in AI Admin Center to configure AI settings.

Use the asset library to create AI assets in your instance.

Create a dataset, generate synthetic data, and publish data collections to evaluate skills and agents.

Create an intent record to standardize a customer input goal and enable multichannel intent detection, AI agent routing, and contact center analytics.

Edit the details and activation status of an intent record.

Edit a custom AI agent that was created for an automation opportunity.

Enable the ServiceNow Otto panel to have your AI assistant perform setup, configuration, and administrative tasks more quickly using natural language prompts.

Deploy a matched AI agent or a new agent to automate a resolution for an identified automation opportunity.

The Assist consumption page provides features that enable you to view the assists consumed on your instance.

Use the Overview dashboard on the Analytics page to monitor key metrics for AI asset activation, adoption, and usage across your organization.

Use the Performance Explorer dashboard on the Analytics page to review and analyze the execution details of assistants and AI agents across your organization.

Run the AI readiness assessment to analyze your instance and evaluate its readiness for AI adoption.

Create and activate a scheduled analysis of your instance records to discover automation opportunities.

Use AI guardrails to detect offensive content, prompt injection attacks, and sensitive topics in generative AI interactions.

Use the AI Admin Center experience to set up, monitor, and manage your AI solutions.

Use the asset library to view and manage the AI assets in your instance.

Review the results of readiness assessments run for your instance.

Use AI Admin Center to view dashboards showing the usage and performance of your AI assets.

View the AI assist consumption on your instance.

Review the automation opportunities that AI Agent Advisor has identified for your instance.

View the top actions you can take right away to adopt AI capabilities on your instance.

Use the system property registry to view and edit the system properties for the AI applications in your instance.

Run an upgrade readiness pre-check and evaluate the current version against the target version to determine if you're ready to upgrade your instance.

Create and activate a scheduled analysis of your instance records to discover automation opportunities.

Use AI Admin Center to set up AI solutions on your instance.

Reactivate a previously deactivated data source analysis.

Deactivate a data source analysis that you no longer want to run for automation opportunity discovery.

Delete a data source analysis that you no longer want to run for automation opportunity discovery.

Edit a scheduled analysis of your instance records.

The AI Agent Advisor automatically discovers automation opportunities in your instance based on actual operational data and helps you to deploy AI agents to implement them.

The following topics provide additional information about the features and properties installed with AI Agent Advisor.

AI Agent Advisor is installed with these roles.

Track the AI agent use and efficiency gain on your instance through the AI Agent Analytics dashboard. The dashboard can reveal trends in how AI agents are used to improve the time to resolution and the number of tasks closed.

Review different ways that you can leverage the Now Assist AI agents application in agentic workflows across the platform.

All ServiceNow AI agents are listed. Depending on your license, not all AI agents may appear in your instance.

Create, manage, and test AI agents and agentic workflows in one centralized space to build self-executing solutions that help you achieve your business goals.

Create, manage, or test AI agents and agentic workflows so that you can create self-executing workflows to help you achieve your business goals.

Turn MCP server connections on or off, automatically deactivate vulnerable tools and activate new tools, and activate the Data Sensitivity Check.

Activate the Data Sensitivity Check so that AI Gateway helps filter personally identifiable information in transactions with the MCP servers that you choose.

Allow agents to call one MCP server tool through AI Gateway, or block every call to it.

Choose which of an MCP server's tools the Data Sensitivity Check inspects.

Prepare your instance for AI Gateway, bring MCP servers into the inventory and approve them, and choose the settings that apply to your MCP server connections.

Register the clients that can connect to an approved MCP server, and then configure agents to call the server through its AI Gateway MCP server URL.

Limit each client to the MCP server tools that it requires by turning on access management for the server and creating client access policies.

Turn MCP server connections off while you investigate a server, turn them back on when you're done, and review each server's activity.

Grant one client access to specific tools on an MCP server.

Remove a client access policy and the access to tools that it grants a client.

Change the name, description, or tools of a client access policy.

Learn how AI Gateway governs connections between AI agents and MCP servers, where you manage it, and how an MCP server is approved for agent traffic.

Review scan results for an MCP server's tools, decide which tools agents can call, and choose the tools that the Data Sensitivity Check applies to.

Route AI agent connections to MCP servers through AI Gateway, so you control which servers and tools agents can reach and what data passes between them.

Use the Overview tab of the AI Gateway settings page to turn connections through AI Gateway on or off, for all MCP servers or for one server.

Use the Policies tab of the AI Gateway settings page to activate the Data Sensitivity Check, which helps filter personally identifiable information in MCP traffic.

Reference topics provide details about the roles and system properties that apply to AI Gateway.

Roles control who can manage AI Gateway, and system properties control AI Gateway behavior on your instance.

Use the Tools tab of the AI Gateway settings page to choose whether tools are automatically deactivated after a security scan or activated after approval.

Turn on access management to limit each client to the tools that its policies grant, or turn it off to let every client use all active tools.

Stop traffic through AI Gateway, for all MCP servers or for one server, while you investigate an issue, and turn it back on when you're done.

Set a system property so that AI stewards can turn on access management and create client access policies for MCP servers.

ServiceNow structures its products and packages in three tiers — Foundation, Advanced, and Prime. Each tier incorporates AI and builds progressively on the previous one with additional AI capabilities, agents, and governance tools.

AI experiences on the ServiceNow AI Platform extend your workflows with AI-driven automation. Use generative AI skills, AI agents, and agentic workflows to automate multi-step tasks, surface relevant answers, and support self-service across your organization.

The following AI agents are available for AI Search.

The following AI agents are available for AI Workflow Factory Prime.

Onboard and view the performance and activity of AI specialists in AI Agent Studio.

Configure your AI specialists in your autonomous workforce in AI Agent Studio.

Onboard and view the performance and activity of AI specialists in the legacy AI Agent Studio.

Configure your AI specialists in your autonomous workforce in the legacy AI Agent Studio.

Reference for Autonomous Workforce provides additional information about the roles and performance metrics associated with AI specialists.

AI specialists in your Autonomous Workforce are designed to handle high-volume, routine requests end to end, giving your team room to focus on the tasks only people can solve.

AI agents can dynamically adjust the actions that are based on the progress and changing conditions of incidents or cases to help ensure that they stay focused on achieving their objectives.

Learn about agentic evaluation runs and the meaning behind different evaluation scores from the agentic evaluation results page.

AI agents continue with follow-up conversations after the AI agent execution is complete.

AI agents continue with follow-up conversations after the AI agent execution is complete.

Identify and block offensive messages that are sent by human agents automatically by enabling AI Guardian in AI agents. With this capability, you can help reduce your agentic workflow or test from being exposed to harmful content.

Disable citations for specific agentic workflows or AI agents in AI Agent Studio where citations aren’t required or involve confidential information.

Disable citations for specific agentic workflows or AI agents in AI Agent Studio where citations aren’t required or involve confidential information.

Long-term memory (LTM) categories define the types of semantic information that a Now Assist AI agent can learn and retain about users over time. You can add new categories and map them to specific agents to personalize agent responses based on accumulated user context.

Find more information about user roles, tables, and the different properties that are installed in ServiceNow Otto AI agents.

Role masking for AI agents and agentic workflows helps users enhance security by enabling them to limit their roles during tool execution and verify that AI agents run with least-access privileges.

Implement security in AI Agent Studio through access control lists (ACLs), user identities, and role filtering with the access control-based security measures in the agentic system.

If any conkeyrefs are broken, re-add them from the doc/source/reuse/domain-separation/domain-separation-overview.dita file.In the short description, edit the first sentence to state whether domain separation is supported or not and add the application name. Keep the conkeyref at the end that describes domain separation.Domain separation is supported for AI agents in the AI Agent Studio. Domain separation enables you to separate data, processes, and administrative tasks into logical groupings called domains. You can control several aspects of this separation, including which users can see and access data.

Build and deploy autonomous AI agents that automate complex workflows. AI Agent Studio is your command center for creating, configuring, and deploying autonomous AI agents that learn your business processes and act on them. Configure agents that learn your processes and handle routine work at scale—while you stay in control and focus on strategic priorities.

Configure AI Control Tower in a logical sequence and track your progress toward a complete setup.

Govern, secure, and measure the value of your AI assets across the enterprise by activating AI Control Tower.

Track and act on the governance work generated across AI Control Tower from a single workspace, including lifecycle tasks, security tasks, change and offboarding requests, and AI recommendations.

When MCP Servers are registered through AI Control Tower, connecting them to an agent happens in two steps: first register the server at the instance level in AI Agent Studio, then add it as a tool within the specific agent.

Add an MCP server manually from AI Control Tower.

Add an MCP server from the MCP Catalog via AI Gateway.

Track and act on the governance work and AI-generated recommendations that AI Control Tower surfaces for you in Activity Center.

The ServiceNow Otto for AI Control Tower plugin (com.sn_aict_genai) includes agents and skills that enhance the AI asset management and governance.

Act on the AI governance work that matters most, review the state of your AI portfolio, and track the value your AI assets deliver on the Home page in AI Control Tower.

Block and unblock AI usage on managed devices by configuring Agent Client Collector (ACC) as a control enforcement point.

After client registration is complete, configure your AI agents to connect to MCP servers through AI Gateway.

Make sure every AI system, MCP server, or AI model is reviewed before deployment by requiring AI steward approval for that asset type and starting approval requests automatically.

Restrict your AI systems to the model providers your organization approves, choose where their requests are processed, and decide what happens to AI systems the approved providers don't support.

Change which discovered AI assets a rule marks as managed, or update its name or description, by editing an existing automation rule.

Connect AWS Bedrock or AWS Bedrock Agent Core to AI Control Tower so policies and AI agent containment using kill switch protocol can reach and act on agents running on AWS.

Monitor AI agents built with AWS Bedrock AgentCore by adding an AWS CloudWatch trace connection. AI Control Tower collects trace data through your AWS credentials and a MID Server, without requiring SDK instrumentation.

Connect Azure AI Foundry to AI Control Tower so that policies and AI agent containment using kill switch protocol can reach and act on agents running on Azure AI Foundry.

Monitor AI agents running on Microsoft Azure by adding an Azure trace connection. AI Control Tower collects trace data through your Azure credentials and a MID Server, without requiring SDK instrumentation.

Keep all ServiceNow Otto traffic in ServiceNow datacenters by opting out of data overflow processing, or opt back in to restore bursting to Microsoft Azure during traffic spikes.

Monitor AI agents running on Google Cloud by adding a GCP Cloud Trace connection. AI Control Tower collects trace data through your Google Cloud credentials and a MID Server, without requiring SDK instrumentation.

Connect Gemini Enterprise Agent Platform to AI Control Tower using a service account and OAuth 2.0 JWT Bearer authentication, so policies and AI agent containment using kill switch protocol can reach and act on agents running on Gemini Enterprise Agent Platform.

Give your organization early access to AI models before they're generally available by accepting the Model Preview Program terms on an eligible instance.

Connect Okta to AI Control Tower so that policies and AI agent containment with kill switch protocol prevent future tokens from being issued to a deactivated AI agent.

Stop your instance from contributing data that ServiceNow uses to improve its AI models and products.

Change how a card presents its data so that a page matches the terminology, level of detail, and reporting focus your organization works with.

Limit your AI systems to the model providers your organization approves, control where AI requests are processed, and see how each change affects the AI systems on your instance before you save it.

Keep a human review between building an AI asset and deploying it by requiring AI steward approval for each asset type and triggering approval requests automatically.

Discover AI assets running on external platforms and hyperscaler environments to populate your AI asset inventory, apply consistent governance, and report on usage in AI Control Tower.

Choose whether your instance contributes data that ServiceNow uses to improve its AI products, and whether ServiceNow Otto traffic can move to Microsoft Azure datacenters during traffic spikes.

Connect AI Control Tower to external AI platforms so you can discover AI assets, collect trace data for monitoring, and synchronize governance across multiple ServiceNow instances.

View and govern your AI portfolio using terms and visualizations tailored to your organization.

Connect AI Control Tower to the platforms where your AI agents run and to your identity provider so that policies and AI agent containment using kill switch protocol can act on agents wherever they're hosted.

Decide what AI data your instance shares with ServiceNow, which AI assets need AI steward approval before deployment, and which AI model providers your AI systems can use.

Collect trace data from AI agents running on supported cloud platforms and monitoring services to generate evaluation metrics, populate AI inventory, and support security monitoring in AI Control Tower.

Set up AI Control Tower so your organization can begin managing AI assets, enforcing governance policies, and tracking AI value.

Ask questions about your AI portfolio in plain language and get answers drawn from your own AI Control Tower records.

Bring AI assets from an external platform into your AI asset inventory by creating an AI connection. AI Control Tower authenticates to the platform using your credentials and imports assets on a schedule, so they're governed and monitored alongside AI systems built natively on ServiceNow.

Connect AI Control Tower to Veza using OAuth 2.0 so that agent risk scores and severity levels from Veza access intelligence appear in the agent map. Since Veza is an enrichment connector, it doesn't import new assets.

Set the conditions for automatically marking discovered AI assets as managed by creating a custom automation rule.

Extend your AI Control Tower pages beyond the built-in configuration options with the ServiceNow Lux Lab for VS Code extension.

Stop trace collection for an established connection without deleting it, so you can reactivate it later.

Get a complete picture of every AI system in your organization by building and maintaining a comprehensive AI asset inventory.

Domain separation is supported for AI Control Tower. Domain separation enables you to separate data, processes, and administrative tasks into logical groupings called domains. You can control several aspects of this separation, including which users can see and access data.

Upgrading AI Risk and Compliance, Enterprise Architecture Workspace, and AI Control Tower Core out of sync with each other temporarily affects the AI system-to-business application association.

Update the configuration of an established trace connection to change its credentials, collection frequency, MID Server, or other settings.

Identify the email notifications that AI Control Tower sends automatically, the events that trigger each notification, and the recipients. These notifications keep AI Stewards, asset owners, and assignees informed so that they can take action promptly on AI asset approvals and lifecycle events.

Learn how AI Control Tower provides centralized visibility and governance across your organization's AI portfolio, connecting strategic planning, asset discovery, monitoring, compliance, and value measurement in a single workspace.

Learn about the fields of the feedback form. Use this form to create or edit the details of a feedback record in AI Control Tower.

Use the Goal form to create goals for your organizational strategic priorities.

Govern AI assets across the life cycle with integrated risk, compliance, security, and privacy oversight in AI Control Tower.

Area-by-area listing of the setup items in guided setup, with cross-references to the topic each item relates to.

Configure AI Control Tower in a logical sequence and track your progress from a single summary page, instead of navigating separate Settings pages.

Several types of components are installed with activation of AI Control Tower, including plugins, user roles, and tables.

View all planning data for a specific AI system in one location. The Plan tab automatically filters goals, intake items, and delivery records to show only those linked to your selected AI system across Strategize, Prioritize, and Execute workflows.

Gain visibility into your organization's AI footprint, manage the lifecycle of AI investments, govern risk and compliance, and measure the business impact of AI with AI Control Tower.

Track and resolve the governance work that AI Control Tower generates as your AI portfolio grows, such as lifecycle tasks, security tasks, cases, and risk assessments.

After you register and manage an MCP server, the AI steward moves it through the onboarding lifecycle before agents can use it.

Measure quantifies the productivity value delivered by your AI systems and tracks their costs. It also reports the net return on AI, enabling you to determine where to scale, fund, or retire AI.

Evaluate the quality and safety of AI interactions across your portfolio using automated scoring, configurable metrics, and trend analysis for both ServiceNow and external AI systems.

The Onboard playbook tracks the MCP server through assessment, testing, and deployment.

The AI Plan tab in AI Control Tower displays when the Strategic Planning application is installed. It requires no additional configuration once installed.

Use the Prioritize tab to add a demand, product idea, or feedback record. Selecting New in any list opens a full form. The form fields differ by record type.

Create a project or epic from the Execute page to track AI-related delivery work in AI Control Tower.

Learn about the fields on the Demand form. Use this form to create or edit a demand from the Prioritize page in AI Control Tower.

Update the details of an existing goal, target, or strategic priority record to reflect changes in organizational direction, scope, or measurement criteria.

Update the details of an existing intake record to reflect changes in scope, priority, or other relevant information.

Update the details of a project or epic from the Execute page in AI Control Tower to reflect changes in scope, state, priority, or other information.