Name: Building Custom Joule Agents in Joule Studio: Defining the Use Case
Uploaded: 2026-2-24
Duration: 212 s

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<div><div><div><div><div><div><div><div><div><div><div><div><div><div><div><div><div><div><div><div><div><div><div><p>In this three-part tutorial series, we’ll walk you through the complete journey of building your own custom AI agent, from defining the business use case to extending its capabilities and finally bringing it to life. Each part builds on the previous one. By the end, you won’t just understand the theory, you’ll have created a fully functional agent tailored to your scenario. </p>  <p>Part One: Define the Use Case - We start by grounding everything in a concrete scenario: Intelligent Maintenance Order Fulfillment. What problem are we solving? What outcome are we aiming for? This foundation ensures the agent is built with purpose.</p></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>

Building Custom Joule Agents in Joule Studio: Defining the Use Case

Let's move on to the use case. I'd like to show you the use case for the Intelligent Maintenance Order. I chose this use case because we also have a Developer tutorial for it, so you'll be able to try it out yourself once you're using Joule Studio. Now let's imagine we have a maintenance planner. His name is Alex and he faces a few challenges but his main goal is to ensure the maintenance order begins on time and that all necessary materials are in stock. He needs to review multiple operations linked to a specific order to ensure there's enough material, reorder from different stock centers or warehouses, and reassign resources if necessary.  And, of course, he needs quick insights into everything he's managing. The ideal scenario is that Alex will message the agent to confirm whether a specific maintenance order can be carried out, and if not, the Agent will provide the requirements and a new estimate. Currently, time -consuming, error-prone form validation is performed without the agent. Alex must switch between different systems, sometimes spreadsheets, sometimes different Fiori apps, sometimes he needs to go into S4, check everything together, and only then make a decision. This demands a lot of time. Plus, there are errors, and Alex can't get a quick and simple "yes or no" answer. When we conducted this type of research to understand your persona, We spoke with business leaders and learned which systems they use, how they work, and what calculations they perform. Sometimes, so that your Agent can later do the same things and save time and effort for the business persona. And that's why for Alex, we've created a maintenance Agent that gives a clear "go or no-go" decision for maintenance orders based on real-time material information. What do we consider when we design the Agent in advance? First, we've designed tools for the Agent. Right now, for this particular use case, we're only adding tool skills as tools. And, there are several tool skills that can be included in this agent to fetch order details, get operations, retrieve different operation components, access materials linked to the operation, and check material stock levels. And, at the moment, this is just a demo system. We're using a custom cloud application service through SAP Build Actions and SAP BTP destinations. Still, even in a real-world scenario, these can be added. For HANA, we could also integrate AI core for document grounding. This is a very, very simple use case, so you can easily replicate it later. But of course, we can expand the number of tools added here, even for this specific use case. Then, the most important part is the instructions. AI can't read your mind, and I think you all know that because when you're chatting with your LLM, you don't always get the right answer. That's why prompt engineering is such a big deal these days. You always need to make sure your instructions are clear and meet your requirements. You can also choose the model. I think you can pick any model that's available on the market. Of course, sometimes it's better to experiment with your specific use case to figure out which model to choose.