Autonomous AI Agents (Agentic) AI and Gen AI are transforming the way custom applications are developed and deployed within ServiceNow. By integrating AI into workflows, enhancing user interactions, and automating tasks, organisations can achieve greater efficiency and better outcomes.
By aligning deployment strategies with these best practices, organisations can maximise the benefits of Agentic AI and stay ahead in the rapidly evolving digital landscape.
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How Many AI Agents is “Too Many” AI Agents?
The deployment and management of AI agents within ServiceNow custom applications should be strategically planned and continuously refined to meet business goals effectively. By determining the appropriate number of agents, defining clear scopes, and actively managing their performance, organisations can enhance automation, efficiency, and user satisfaction, while minimising risks. Regular monitoring, training, and feedback loops are essential to ensure that AI agents evolve and remain aligned with business needs.
The number of AI agents depends on the use cases and the specific functions you want to automate. Here are some factors to consider:
Factors to Consider:
- Task Complexity: If you have a wide variety of distinct processes, such as incident resolution, knowledge management, HR service delivery, and IT support, you may need separate agents to handle each area. For simpler processes, one multi-purpose AI agent might suffice.
- Volume of Requests: High-volume areas like IT support or customer service benefit from multiple agents to handle different requests concurrently, ensuring smooth operations.
- Business Units/Departments: For larger organisations with different departments, you might need AI agents tailored to the specific needs of each department (e.g., IT, HR, Finance).
- Scalability: Start with a smaller number of AI agents and scale as needed. You can initially deploy a couple of focused agents and expand based on performance data and evolving requirements.
AI Agent Scope
The scope of AI agents refers to the specific tasks, data, and processes they can interact with or control. Defining clear scopes ensures that the agents work efficiently and don't overstep their boundaries.
Factors to Consider:
- Task Definition: Clearly define the tasks each AI agent should handle.
- Data Access: Determine which data the AI agents can access. For example, a virtual assistant may only need access to knowledge base articles, while an incident management agent may need access to sensitive incident-related data.
- Decision-Making Boundaries: Define how much autonomy each agent has. For example, should the agent be able to resolve incidents automatically or should it only provide recommendations to human agents?
- Integration Scope: Define what systems and services the AI agents should be integrated with. Should they only work within the ServiceNow platform, or should they interact with external systems like email, chat tools, or third-party applications?
Advanced Analytics for Data-Driven Decision Making
AI can analyse large volumes of data to uncover trends, anomalies, or hidden insights. This can drive more data-informed decisions by providing stakeholders with actionable insights that would have been difficult to obtain manually.
AI-powered automation can optimise workflows and business processes by anticipating user needs, recommending actions, or triggering processes based on data patterns. With agentic AI, workflows can become more autonomous, enabling the system to handle tasks such as ticket classification, issue resolution, or task assignments with minimal human intervention.
Factors to Consider:
- Integrate AI early in the design: AI capabilities, such as natural language processing (NLP), machine learning (ML), and predictive analytics, should be considered during the initial design of the custom application to ensure a seamless integration.
- Incorporate real-time analytics: Integrate AI-driven analytics into custom applications to provide real-time data insights. This could include dashboards that use AI to offer predictive insights, automate reports, or identify key metrics that require attention.
- Ensure data quality with Raptor DB and Zero Copy: The effectiveness of AI and ML models is heavily reliant on the quality of data. Ensure proper data governance, quality checks, and data cleansing during deployment to ensure the model outputs are accurate and actionable.
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Enhanced User Experience Through AI-Driven Interfaces
AI-powered interfaces like virtual assistants (e.g., Agentic AI Search, ServiceNow Virtual Agent) can provide users with more intuitive interactions. These AI agents can engage users through conversational interfaces and guide them through processes, reducing friction and enhancing overall user experience.
Factors to Consider:
- Design with AI agents in mind: When creating custom applications, consider how AI agents will interact with users. Build user interfaces that are compatible with conversational UI and ensure smooth transitions between human and AI interactions.
- Personalise experiences: Use AI to personalise user experiences by adapting workflows, notifications, and suggestions based on user preferences, behavior, and history.
- Test AI interaction thoroughly: Test AI-driven interactions rigorously to ensure that they understand user queries correctly and offer helpful responses.
Faster Deployment with AI-Powered DevOps and CI/CD Automation
AI can accelerate deployment processes by enhancing DevOps and continuous integration/continuous deployment (CI/CD) pipelines. It can automatically identify code issues, test custom applications for performance, and optimise the deployment process by suggesting best practices or predicting potential deployment failures.
Factors to Consider:
- Automate testing with AI: Use AI-driven test automation to detect defects and optimise code during the CI/CD process. AI tools can also help identify performance bottlenecks, security vulnerabilities, or code inefficiencies before deployment.
- Utilise predictive analytics for deployment: Implement AI models that predict deployment risks or delays based on historical data, allowing teams to take proactive steps to mitigate issues.
- AI-driven monitoring post-deployment: After deployment, use AI for predictive monitoring of the custom application’s performance, identifying issues before they impact users.
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Wow Wow Wow Hold Your Horses !! Best Practice Guide for Scoped Applications
In the world of agentic AI and AI, where automation and intelligent systems are rapidly transforming business operations, it's easy to get caught up in the excitement of cutting-edge technologies. However, amidst this innovation, we must not forget the foundational principles of building custom applications on ServiceNow. While AI-driven solutions offer immense potential, the core best practices for development—such as security, performance optimisation, and user-centric design—remain crucial for creating reliable, scalable, and effective applications. Balancing advanced AI capabilities with these fundamentals ensures that ServiceNow scoped applications deliver both innovation and stability.
Best Practice Guide for Building Custom Applications
- Define Clear Objectives and Requirements
- Engage stakeholders early to gather detailed requirements.
- Use Agile methodologies to refine and iterate on requirements.
- Document functional and non-functional requirements clearly.
- Establish key performance indicators (KPIs) for success.
Reference: ServiceNow Application Development Guide
- Use Scoped Applications
- Create a scoped application to ensure modularity and security.
- Define meaningful namespaces to avoid conflicts with out-of-the-box (OOB) functionality.
- Leverage ServiceNow Studio for development and application lifecycle management.
Reference: Scoped Applications in ServiceNow
App Engine Academy #:1 Custom App Development Best Practices
- Follow Development Best Practices
- Utilise Application Development Lifecycle (ADLC) processes.
- Maintain clear separation between configuration and customisation.
- Follow naming conventions for scripts, tables, and UI components.
- Use GlideRecord API efficiently and avoid unnecessary queries.
- Implement asynchronous processing (e.g., event-driven workflows, scheduled jobs) where appropriate.
Reference: ServiceNow Development Best Practices
- Ensure Performance Optimisation
- Optimise database queries and avoid large data fetches.
- Use indexed fields for frequently queried data.
- Minimise client-side processing to reduce load times.
- Leverage caching mechanisms and script includes for reusable logic.
Reference: Performance Best Practices
- Security and Access Control
- Use Access Control Rules (ACLs) to enforce security at field, record, and table levels.
- Implement role-based access control (RBAC) to ensure least privilege principle.
- Avoid hardcoding credentials and API keys in scripts.
- Conduct regular security reviews and penetration testing.
Reference: ServiceNow Security Best Practices
- Leverage Platform Capabilities
- Utilise Flow Designer and Business Rules for process automation.
- Use IntegrationHub for external system integrations.
- Implement Service Catalog and Virtual Agent for user self-service.
- Take advantage of Performance Analytics for data-driven decision-making.
Reference: Flow Designer Documentation
- Implement Proper Version Control and CI/CD
- Use ServiceNow’s Source Control Integration for managing application versions.
- Maintain separate development, testing, and production instances.
- Leverage Automated Test Framework (ATF) for regression testing.
- Implement CI/CD pipelines using ServiceNow DevOps.
Reference: ServiceNow DevOps
- Ensure Maintainability and Documentation
- Keep detailed documentation using ServiceNow's Embedded Help or Knowledge Base.
- Comment scripts effectively to improve readability and maintainability.
- Train users and administrators for smooth adoption.
- Regularly review and refactor code for efficiency and compliance.
Reference: ServiceNow Documentation Best Practices
- Monitor and Optimise Performance
- Use ServiceNow Performance Analytics and logs for monitoring.
- Analyse instance health via ServiceNow Instance Security Center.
- Optimise scheduled jobs and background scripts to reduce processing load.
Reference: Monitoring Best Practices
- Engage with the ServiceNow Community
- Stay updated with the latest ServiceNow releases and best practices.
- Participate in ServiceNow Developer forums and Knowledge conferences.
- Utilise ServiceNow Developer Instance for prototyping and testing.
Reference: ServiceNow Community
Conclusion
As we embrace the potential of agentic AI and AI in building scoped applications on ServiceNow, it’s essential to stay grounded in the fundamental principles of development. By focusing on best practices such as security, performance optimisation, modular design, and user experience, we ensure that AI-driven solutions enhance the reliability and scalability of custom applications. The integration of intelligent systems should complement, not replace, the core practices that make ServiceNow a powerful platform for automation and service management. Ultimately, a thoughtful balance of advanced AI capabilities with solid development fundamentals will drive the long-term success of custom applications and provide lasting value to businesses.
