Building a Use Case from Scratch for Agentic AI in ServiceNow: Key Takeaways from Webinar

From day to day, Agentic AI is proving its efficiency when it comes to ensuring autonomous workflows within the digital ecosystem of an enterprise. However, many still might wonder how to build a ServiceNow Agentic AI use case from scratch.Recently, we had a webinar dedicated to ServiceNow AI Agent use cases and their construction. Here we are going to share some key takeaways and lifehacks from that event. We hope they will help you understand the functioning of Agentic AI better.

Identifying a Business Challenge 

First of all, it’s important to take into account that different people might understand the role and capabilities of Agentic AI differently, from performing basic chatbot functions to solving complex workflow autonomy challenges. Actually, an AI Agent is capable of both, so the first step in building a use case of an AI Agent in ServiceNow is to clearly identify your challenge and goal.

An AI Agent from ServiceNow is capable of performing different tasks and solving different challenges when they are clearly identified. For example, an HR onboarding paperwork requires something more than just analyzing data at putting it into a table. The onboarding process is complex and includes many stages that depend on one another; that’s why an AI Agent should know specifically what type of data it will be dealing with and for what purpose.

(Photo Credit: Servicenow)

You can also apply it to processes like IT helpdesk ticket triage or customer service inquiries, but in any case, streamlined processing and automation are only possible when the task is clear and the challenge has its well-specified framework.

Mapping Workflows that Benefit from an AI Agent

In addition to specifying the general challenge, we suggest that you select processes where Agentic AI can add the most value. Here, the focus is on finding points where automation removes friction. Yes, manual handoffs should be reduced, but some complex tasks should still be under the charge of professionals.

When it comes to the IT helpdesk, Agentic AI can perfectly deal with triaging incidents automatically. When we talk about the HR onboarding paperwork, Agentic AI might streamline onboarding by triggering background checks, equipment requests, and account setup in one flow. 

In the customer support case, Agentic AI can easily do the necessary research and collect data from different sources, and put the result into the customer support case even before human involvement. However, this only works for so-called deterministic workflows and might not be as effective when some decisions in-between the steps are necessary.

The key is to align the agent with repeatable, rules-based steps where it can act consistently. At the same time, some space should still be left for human oversight because some sensitive issues might hardly be regulated at this stage of Agentic AI evolution.

(Photo Credit: Servicenow)

Designing the Conversation & Intent Flow

You should take into account that using tools such as the AI Agent Orchestrator, you can put different AI agents to work within the same ecosystem, while still making them act independently upon their specific tasks. However, it’s crucial to define how the agent understands user requests, gathers context, and guides them through the next steps using natural language. This will ensure that interactions will feel intuitive and aligned with business goals.

Also, before designing the conversation and intent flow, you need to make sure that your platform is capable of working with agentic AI. This is especially important when it’s connected to third-party databases because here security concerns might arise.

Adding Task Execution 

Last but not least, when all the stages above are complete, you should add task execution. To make the task execution as flawless and smooth as possible, make sure that the task within your platform is deterministic, meaning it has a clear and well-defined framework. For some more complex operations, human execution remains safer and thus, more effective so far. 

Upon doing so, the platform will connect the AI agent to the ecosystem’s workflows and data. The agent takes real actions based on the challenges and goals you set.The scope of task execution is rather wide — from creating catalog requests, updating records, to resolving incidents. Upon that stage, the AI agent moves from conversation to concrete results.

The Final Thought

This was just a brief and simple summary of our webinar touching on the examples of good and bad use cases of ServiceNow Agentic AI in workflow automation. Explore more about the perspectives of the innovations and some pitfalls that might occur on your way to successful workflow automation.

Kostya Bazanov, Managing Director, Oct 13, 2025