Examples of using AI agents
Summarize
Summary of Examples of using AI agents
The Now Assist AI agents application empowers ServiceNow customers to automate and enhance various business processes across their enterprise. These AI agents operate independently or within hierarchical structures to streamline workflows, reduce manual tasks, and boost service delivery efficiency. They are applicable across multiple departments including IT Service Management, Customer Service Management, and Human Resources.
Show less
Key Features
- Incident Management and Resolution: AI agents automate the entire incident lifecycle by retrieving incident details, searching historical data and knowledge bases, and proposing resolutions. They can close incidents automatically when confidence is high or escalate complex issues to specialists, reducing resolution times while maintaining quality.
- Case Management Automation: AI agents support customer service by analyzing cases, referencing historical and knowledge base information, and recommending next steps. This enables teams to manage higher case volumes with consistent response quality.
- Cross-Functional Process Automation: Agents coordinate workflows spanning departments such as HR, IT, facilities, and finance. For example, onboarding agents manage tasks like access provisioning and workspace setup, track progress, send reminders, escalate delays, and communicate status updates to stakeholders.
- Knowledge Management and Content Discovery: AI agents assist users by understanding queries, searching multiple knowledge sources, and delivering role-appropriate, contextualized information to facilitate task completion and issue resolution.
- Virtual Agent Enhancement: AI agents augment Virtual Agent interactions by providing intelligent responses, retrieving relevant data, and executing actions, thereby enabling more sophisticated and helpful conversational experiences.
Implementation Considerations
- Begin with well-defined, repetitive processes that have measurable success criteria.
- Ensure sufficient training data and robust knowledge base content for agent reference.
- Set confidence thresholds to balance automated actions and human review appropriately.
- Implement monitoring and quality assurance to evaluate and improve agent performance.
- Plan for iterative enhancements driven by agent interactions and outcomes.
- Configure memory sharing suitably within agent hierarchies to support complex workflows.
- Consider scalability requirements when designing agent structures to meet organizational demands.
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.
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.
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.
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