Unleashing SAP Data with Camunda’s Agentic AI: A New Era of Process Intelligence

How to free your SAP data

If you run SAP, you already know the drill: it’s the single source of truth for much of your mission-critical data, yet tapping that data for anything new often feels overwhelming and complex. You may just want to execute a simple inquiry from another application or process, but find it hard to justify the investment it might take to accomplish the task.

Camunda was born to break down those walls. Its open architecture lets you drop a modern orchestration layer on top of SAP and every other system in your landscape—no rip-and-replace, no multi-year science project. One model, one engine, total visibility.

Meet agentic orchestration + the SAP connector

Agentic orchestration is a fancy name for “let software agents think for themselves” dynamically deciding what to do next, reacting to events, even looping humans in when judgment is required.

In Camunda, that intelligence shows up as:

• BPMN ad-hoc sub-processes for the on-the-fly flexibility required for true agentic orchestration.
• Event-driven sub-processes that fire the instant something changes for fast processing and resolution.
• AI connector-powered planning loops that serve as each agent’s “brain” to achieve its goals.

Our SAP OData connector slides right into your AI agent’s toolbox. To an agent, calling SAP now looks exactly like calling a REST API, a Slack channel, or anything else. For example,

1. You build an agent that should “Provide a list of all partners.”
2. The agent fetches the data through the SAP connector.
3. Camunda will handle any retries, compensations, auditing, and monitoring so you don’t have to.

And remember, quantity does not always imply quality. You can have simple agentic orchestration that includes only SAP in your AI agent’s toolbox. Even this simple construct can enrich your data and your user experience.

Why you’ll care

Before we get into the details, let’s zoom out for a second. The SAP OData connector isn’t just another widget on your integration checklist—it can be that missing link that brings simple access to your SAP data and keeps your applications in sync.

The benefits of this marriage are great. For example, including the SAP connector in your AI agent toolbox provides end-to-end visibility so that every SAP request along with your API calls, human intervention, and more all land in a single process instance for real-time monitoring and tight compliance.

It also positions you for flexible and fast iterations. You can add more tools and update your BPMN diagram, hit deploy, and the planning loop adapts instantly.

Check out this demonstration

We think the best way to see the power of this combination is to show it to you, so we have prepared a short demonstration that walks you through the power of AI and SAP using the simple process model shown below.

Assume that you have a simple process that has three (3) major elements:

• An AI agent: This is the LLM that processes an incoming request and decides what tools in the toolbox to use in order to resolve the request.
• An SAP OData connector: this will respond to a request for information on a specific partner or all partners using natural language to make the SAP request.
• Human data display: This will display the data retrieved from SAP to the requestor as well as provide the opportunity to refine or further the request.

To better understand the power of this process, let’s look at the natural language prompt we provided to the SAP connector:

`This variable looks for either the list of partners or details of a specific partner. To get all partners the variable should be exactly ‘A_BusinessPartner’. To get details on a specific partner, you need the ‘BusinessPartnerID’ and then you can request details for that partner by entering A-BusinessPartern(‘partnerID’). e.g. if the BusinessPartnerID was 1 – you would enter exactly A_BusinessPartner(‘1’)`

The prompt clearly defines which parameters to use to obtain all partners or details for a single partner. So, it seems we have created a simple AI agent that will query for partners for us, but it really does more than that. Stay tuned.

Demonstration flow

We start the process by making a data request which is done by filling in a form. In this case, we will ask for all partners created in 2025.

It is important to note that in our simple example,  the SAP connector doesn’t have the query parameters to do a query with time constraints. It can give us the partners or information on a specific partner, but doesn’t have the query to ask for partners in a specific year or date/time range.

Although the AI agent isn’t able to run a specific query with dates, it can get all the partners and then contextualize the information retrieved to answer the query by the end user (subset the list by only those created in 2025). That is what is then displayed to the user along with the date that the partner record was created in SAP.

Let’s take a look at the combination of these elements working together by accessing this demonstration.

Did you notice that we could also refine our request to see all the partners, but the call to SAP didn’t have to take place?

The AI agent already knows it has all that data because it used it for the first request. It just reformulates how to respond and provide a list of all the partners.

And, wait, what about asking it for details about a specific partner by name? It does have to query SAP again, but if you remember, our prompt doesn’t query by name, it queries by partner ID. Not a problem for our AI agent. This means the end user can simply “converse” with the agent to refine information and doesn’t have to be a query expert to find the data they are looking for and then parsing it to get just what they need. You can see this in action in the demo video we mentioned earlier.

Under the hood: How agentic AI plays with SAP

Agentic orchestration means making informed decisions in real time, tracking the current context, understanding it, and applying that insight to improve the user experience.

Below are the four core mechanics that let it treat SAP like a living, breathing partner rather than a rigid back-end.

• Planning loop – After each step, the agent re-evaluates context and picks the smartest next move.
• Short-term memory – Process variables can store the conversation state across multiple SAP calls.
• Long-term memory – Need deeper insight? You can pull historical cases from a vector store on demand.
• Multi-agent choreography – The ad-hoc sub-processes of tools for the AI agent coordinates the tools and uses what is needed to achieve its goal.

Business numbers that make the board lean in

Let’s translate this technical sizzle into meaningful numbers. Camunda AI orchestration plus the SAP connector doesn’t just streamline processes—it turns them into efficient conversationalists that provide valuable information quickly and efficiently without the need to “call a friend” to make the request. Your end users do not need to be SAP-skilled to benefit from this approach.

Check out these achievable KPIs from some of Camunda’s customers.

The bottom line

With Camunda’s agentic orchestration engine and our SAP OData connector, you no longer have to choose between SAP stability and business agility. You keep governance, gain AI-powered flexibility, and finally unlock the process intelligence hiding in your most valuable data sources.

Next steps

What to try this out for yourself? Then, think about getting started with Camunda today.

Want more information about the SAP OData Connector? Check out Camunda Marketplace.

Or just learn more about Camunda’s SAP integration.

Additional Resources

Take a look at these additional resources for more information.

• Why We Developed a Camunda SAP Integration Solution
• Documentation on SAP Integration
• The Benefits of BPMN AI Agents
• Guide to Adding a Tool for an AI Agent