Agentic AI (AI Agent) Development Guidelines and Use Cases (Hands-on Experience)

[John Zhang1]

With the Yokohama release, ServiceNow introduced specialized AI agents that optimize operations. These agents handle both repetitive and complex tasks, boosting efficiency and allowing employees to focus on strategic work.

By adopting Agentic AI, organizations improve automation, reduce manual workloads, and enhance productivity, driving a transformative shift in business operations.

ServiceNow Agentic AI streamlines workflows across IT, HR, security, and customer service. For example, in an IT service desk, Agentic AI can manage password resets by understanding requests, verifying security, and executing the reset automatically.

The propose of this blog is to walkthrough the Agentic AI development steps so you are able to use this blog as a reference to build your own Agentic AI solution.

Objectives

• AI Agent Control Tower Process
• Pre-requirement to use AI Agent Studio
• Agentic AI Development steps and use cases
• ServiceNow AI Agents Categories.
• Summary
• Reference

AI Agent Control Tower Process

A system where specialized AI agents collaborate as a team to achieve specific business outcomes. These agents can:

1. Autonomously analyze complex situations
2. Generate strategic insights
3. Execute actions aligned with business goals

AI agents utilize various tools to execute defined use cases. AI Agent Studio guides users in leveraging AI agents effectively, while AI Orchestrator ensures seamless collaboration among them. Establishing an AI Agent Control Tower enables governance, tracking of use cases, and measuring value across the enterprise.

AI Agent Orchectrator

In Agentic AI within ServiceNow's AI Agent Studio, the Orchestrator plays a crucial role in coordinating and managing workflows across multiple AI agents and enterprise systems. It acts as the central control mechanism, ensuring that different AI agents execute their tasks in a structured, efficient, and policy-compliant manner.

The Orchestrator acts as a coordinator, facilitating communication between agents to complete complex tasks. It can also retrieve missing context from users when needed. AI agents operate iteratively, seeking assistance from the Orchestrator if they encounter difficulties.

Orchestrator's Role in AI Agent Studio Use Cases

1. Task Coordination & Execution - manages the interaction between multiple AI agents, ensuring that tasks are executed in the correct sequence.
2. Dynamic Decision-Making - Can invoke AI models dynamically based on the scenario.
3. Seamless Integration with Enterprise Systems - Works with ServiceNow workflows, CMDB, and third-party APIs to enable end-to-end automation.
4. Handling Multi-Step Workflows - Enables chained execution of AI-driven tasks among multiple AI Agents.
5. Policy & Governance Enforcement - Enforces governance rules to ensure that AI agents adhere to compliance and security standards.

Pre-requirement to use AI Agent Studio 

1. AI Agent Studio  - new AI Agent Development tool in Yokohama release
2. Now Assist Skill Kit - new feature in Xanadu release.  This feature manages and coordinates multiple AI agents, ensuring they work harmoniously to achieve defined business objectives. It facilitates seamless collaboration among native and third-party AI agents across departments like IT, HR, and customer service
3. The requied roles for both Now Assist and AI Agent Studio:
   1. now_assist_panel_user
   2. sn_skill_builder.admin
   3. now.assist.creator
   4. sn_aia.admin

Agentic AI Development steps and use cases

Create Use Cases

Step 1 - Open Now Assist AI agent Studio: Search Now Assist AI agents-->Overview--Manage use case and AI Agents (All > AI Agent Studio > Create and manage > Use cases > New)

Define Use Case after clicking New button

Use Case Sample 1 - Get User Roles:

Use Case Sample 2 (Ref. 3)

Step 2 - Create AI Agents

AI Agent Use Case 

1. Use Case 1 (Ref.1)

Use Case 2

Use Case 3 (Ref. 3)

Step 3 - Add tools and information

In ServiceNow AI Agent Studio, the purpose of adding tools and information in AI Agent creation is to enhance the AI agent’s capabilities by enabling it to perform specific tasks, retrieve relevant data, and provide more accurate responses to users.

There are many options of tools indicated in the following screenshot.  I am going to have each tool use case for your reference.

Tool RAG Use Case (Ref. 3)

Tool Sub flow use case:

Tool Flow Action use cases:

AI Agent Use Test Use Cases

Test AI Agent Use Case 1 - Project Portfolio Agent

Test AI Agent Use Case 2 - Project Portfolio GxP Policy Agent

ServiceNow AI Agent Categories (Ref. 2)

Nicola Attico published a good AI Agent category related linkedin to define three categories 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐭𝐚𝐛𝐥𝐞 [𝐬𝐧_𝐚𝐢𝐚_𝐚𝐠𝐞𝐧𝐭]: 1) Applicattion based; 2) Platform-based; 3) Spoke-based.

ServiceNow AI Learning patch (Ref. 4)

Summary

In ServiceNow, Use Cases define business needs, AI Agents execute specific tasks, and AI Agent Orchestrators manage workflows to automate processes, improve efficiency, and enhance service delivery.

Refference (Ref)

1. AI Agents: Hands-On Demo + Setup Walkthrough
2. The ServiceNow 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐄𝐜𝐨𝐬𝐲𝐬𝐭𝐞𝐦
3. AI Agent Studio: The Next Evolution in Intelligent Workflows
4. 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐀𝐈 𝐰𝐢𝐭𝐡 𝐒𝐞𝐫𝐯𝐢𝐜𝐞𝐍𝐨𝐰
5. Create your first Assistive AI Agent with the Now Assist Skill Kit
6. A simple AI Agent - the most valuable solution I have ever created
7. AI Agent Studio
8. 4 𝐏𝐨𝐰𝐞𝐫𝐟𝐮𝐥 𝐕𝐢𝐬𝐮𝐚𝐥 𝐌𝐚𝐩𝐬 𝐨𝐟 ServiceNow 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞𝐬 

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[NickellC]

First great article.

Certainly! In ServiceNow, the integration of Use Cases, AI Agents, and AI Agent Orchestrators forms a powerful framework for automating IT and business processes. Here’s a deeper exploration of each component and how they work together to achieve improved efficiency and service delivery.

1. Use Cases

Definition: Use Cases in ServiceNow articulate specific business needs or requirements that help define how the platform will solve problems or improve processes. They serve as a blueprint for identifying tasks that can benefit from automation or enhanced workflows.

Examples:

• Automated Incident Resolution: Creating a use case identifying that repetitive incidents (like password resets) can be handled through automation.
• Self-Service Enhancements: Defining the need for a chatbot to assist users in submitting service requests or resolving issues without agent intervention.

Benefits:

• Clear articulation of what needs to be addressed within the business context.
• Helps prioritize projects and allocate resources effectively based on organization needs.

2. AI Agents

Definition: AI Agents are intelligent virtual assistants that leverage artificial intelligence and machine learning capabilities to perform tasks, answer queries, and assist users through natural language processing.

Functions:

• Incident Management: AI Agents can automatically capture and categorize incidents based on user input, streamlining the process of logging incidents.
• Self-Service Support: By utilizing chatbots, AI Agents can provide immediate responses to frequently asked questions and guide users through troubleshooting steps.
• Recommendation Engine: They can suggest knowledge articles or solutions based on user inquiries and previous incidents.

Example:

• An AI virtual agent integrated into a ServiceNow instance that assists users in checking incident statuses, or creating service requests by understanding natural language input.

Benefits:

• Enhances user experience by providing immediate, automated assistance.
• Reduces the workload of service desk agents, allowing them to focus on more complex issues.

3. AI Agent Orchestrators

Definition: AI Agent Orchestrators are responsible for managing and coordinating multiple AI Agents and their interactions, including how they interface with the broader ServiceNow workflows and third-party services.

Functions:

• Workflow Automation: Orchestrators define the workflows that detail how tasks are executed, including invoking different AI Agents depending on the context of the service request.
• Integration with Other Processes: They can connect AI agents to other ServiceNow modules, such as ITSM, HR Service Delivery, or ITOM, ensuring a seamless flow of tasks across the platform.
• Performance Monitoring: Track the performance of AI agents and workflows, providing insights for continuous improvement.

Example:

• An orchestrator that manages a workflow where a user requests a new laptop. The workflow may:
  1. Verify user eligibility.
  2. Assign tasks to an AI Agent for ordering the laptop.
  3. Notify the user of approval and order status.

Benefits:

• Streamlines processes by providing a structured approach to AI-driven workflows.
• Improves efficiency by automating repetitive tasks across various domains, leading to faster service delivery and higher satisfaction rates.

Conclusion

The interplay between Use Cases, AI Agents, and AI Agent Orchestrators in ServiceNow creates a robust framework for addressing business challenges through automation. This synergy not only enhances operational efficiency but also fosters a better experience for users seeking IT and business services. By harnessing these components, organizations can effectively streamline service delivery and adapt to evolving demands.

[Roborts]

This was a fascinating read – especially how Agentic AI is moving from single-task execution to multi-step orchestration across systems. The emphasis on autonomy and responsible development is well-placed, but I think one layer that deserves more attention in these discussions is how we validate agent behavior at scale.

As these agents begin interacting with dynamic environments, APIs, and even third-party systems, there's a growing need for rigorous testing frameworks – not just unit testing, but performance testing, fail-safe scenario simulation, and input-output consistency across edge cases.

I've seen firsthand how even well-designed agents can behave unpredictably without automated QA layers in place –especially when orchestration spans multiple systems. Would love to see future discussions on how AI agents can be stress-tested like traditional APIs, or how testing principles evolve alongside these intelligent workflows.

[TheAtlas_0000]

What is the difference of Issue Auto Resolution from using AI Agents in providing resolution and immediate action to user's incidents or queries? 

[Sarda702]

Does anyone have the editable slide of this image.

[DevH]

"This is an excellent walkthrough of how Agentic AI is shaping intelligent automation in ServiceNow! The step-by-step guide and real use cases make it really easy to understand and apply in real scenarios. Thanks for sharing such valuable hands-on insights!"

[Praveen105]

Great walkthrough of ServiceNow Agentic AI, Clear steps on building use cases, creating AI Agents, and orchestrating workflows with the control tower. A must read for anyone looking to accelerate automation.