Examples of using AI agents

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

AI agents Overview

AI agents in the Now Assist application provide automated solutions for a wide range of business processes and operational challenges. These agents can work independently or as part of hierarchical structures to handle complex workflows, reduce manual effort, and improve service delivery efficiency. You can leverage AI agents across multiple departments including IT Service Management, Customer Service Management, and Human Resources.

Incident Management and Resolution

AI agents can automate the entire incident lifecycle from initial triage to resolution. When an incident is created and assigned to a designated service desk user, the agent automatically retrieves incident details, searches for similar historical incidents, and analyzes relevant knowledge base articles. Based on this research, the agent determines an appropriate resolution path.

If the agent has high confidence in the solution, it posts the resolution in the work notes and closes the incident automatically. When confidence is lower, the agent documents its findings and reassigns the incident to a technical support specialist for review. This approach reduces mean time to resolution while ensuring quality control for complex issues.

Case Management Automation

Similar to incident management, AI agents can handle case workflows in customer service scenarios. Agents analyze case details, match them with historical cases, search knowledge articles, and provide recommended solutions or next steps. This automation helps customer service teams handle higher volumes of cases while maintaining consistent quality in responses.

System Remediation and Maintenance

AI agents can perform automated system remediation tasks when they identify common issues with clear solutions. For example, if an agent determines that restarting an application will resolve a reported problem, it can invoke specialized agents such as Digital Employee Experience (DEX) agents to execute the required system actions. This capability enables true zero-touch resolution for many common technical issues.

Web Automation

Web automation agents can interact with web-based systems and interfaces to retrieve information, submit forms, or perform routine tasks across multiple platforms. These agents extend automation capabilities beyond the ServiceNow platform, enabling end-to-end process automation that spans multiple systems and applications.

Hierarchical Agent Structures for Complex Workflows

Organizations can build sophisticated multi-agent systems using hierarchical structures where parent agents coordinate multiple child agents. Each child agent specializes in specific tasks or domains, and the parent agent orchestrates their activities to solve complex business problems.

For example, a comprehensive service desk automation solution might include a parent agent that coordinates specialized child agents for incident classification, knowledge retrieval, similar incident analysis, remediation actions, and communication. This modular approach allows organizations to scale their automation capabilities and customize workflows for different business units while maintaining a consistent base structure.

Cross-Functional Process Automation

AI agents can orchestrate workflows that span multiple departments and systems. For instance, an onboarding agent might coordinate tasks across HR, IT, facilities, and finance departments, ensuring new employees receive proper access, equipment, workspace setup, and orientation materials. The agent can track progress, send reminders, escalate delays, and provide status updates to stakeholders throughout the process.

Knowledge Management and Content Discovery

AI agents can assist users in finding relevant information by analyzing queries, searching across multiple knowledge sources, and presenting contextualized results. These agents understand the user's intent, consider their role and permissions, and deliver targeted information that helps them complete tasks or resolve issues independently.

Proactive Issue Detection and Prevention

AI agents can monitor system metrics, user behavior patterns, and operational data to identify potential issues before they impact users. When the agent detects anomalies or concerning trends, it can take preventive actions, create proactive tasks for support teams, or notify stakeholders with recommendations for intervention.

Virtual Agent Enhancement

AI agents can work behind the scenes to enhance Virtual Agent conversations by providing intelligent responses, retrieving relevant information, and executing actions on behalf of users. This integration creates more capable and helpful conversational experiences that can handle complex queries and multi-step processes.

Customization and Extension Capabilities

Organizations can extend platform-provided agents or create custom agents tailored to their specific business needs. Business units can clone base agent hierarchies and add their own specialized child agents to address unique requirements while still benefiting from platform updates to the core agent structure. This extensibility model enables both standardization and customization within the same framework.

Implementation Considerations

When implementing AI agents, organizations should consider the following factors:

• Start with well-defined, repetitive processes that have clear success criteria
• Ensure adequate training data and knowledge base content for agents to reference
• Define appropriate confidence thresholds for automated actions versus human review
• Establish monitoring and quality assurance processes to track agent performance
• Plan for iterative improvements based on agent interaction data and outcomes
• Configure memory sharing appropriately for different hierarchy structures
• Consider scalability requirements when designing complex agent hierarchies