--- sourceDocument: Brazil Enable AI sourceDocumentLink: https://www.servicenow.com/docs/r/intelligent-experiences Release : - brazil ft:locale : - en-US ft:publication_title : - Brazil Enable AI ft:clusterId : - platai bundleId : - platai workflow : - Platform --- # Examples of using AI agents # Examples of using AI agents {#ariaid-title1} Release version: Brazil Updated September 10, 2026 ![](https://www.servicenow.com/docs/portal-asset/ico-clock) 2 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 Examples of using AI agents The Now Assist AI agents application enables ServiceNow customers to automate and optimize a variety of business processes and operational workflows. These AI agents can operate autonomously or within hierarchical structures to reduce manual effort, accelerate service delivery, and enhance workflow efficiency across departments such as IT Service Management, Customer Service Management, and Human Resources. Show full answer Show less ## Key Features * **Incident Management and Resolution:** AI agents automate the incident lifecycle by retrieving incident details, searching historical records and knowledge bases, and determining resolution paths. High-confidence solutions allow agents to close incidents automatically, while lower-confidence cases are escalated for human review, improving resolution speed and quality control. * **Case Management Automation:** Similar to incidents, AI agents analyze case details in customer service workflows to recommend solutions based on historical cases and knowledge articles, enabling teams to efficiently handle higher case volumes without sacrificing response quality. * **Cross-Functional Process Automation:** AI agents can orchestrate complex workflows that span multiple departments, such as employee onboarding, coordinating tasks across HR, IT, facilities, and finance. They track progress, send reminders, escalate delays, and update stakeholders to ensure smooth end-to-end process execution. * **Knowledge Management and Content Discovery:** By analyzing user queries and considering user roles and permissions, AI agents assist in delivering targeted, contextualized information from multiple knowledge sources to help users resolve issues or complete tasks independently. * **Virtual Agent Enhancement:** AI agents augment Virtual Agent conversations by providing intelligent responses, retrieving relevant information, and executing actions, resulting in more capable and helpful conversational experiences that handle complex queries and workflows. ## Implementation Considerations * Begin with well-defined, repetitive processes that have measurable success criteria. * Ensure sufficient training data and comprehensive knowledge base content for agent reference. * Set appropriate confidence thresholds to determine when agents act autonomously or escalate to human agents. * Establish monitoring and quality assurance practices to continuously assess agent performance. * Plan for iterative improvements based on interaction data and outcomes. * Configure memory sharing carefully when designing hierarchical agent structures. * Consider scalability requirements for complex agent hierarchies to support organizational growth. 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